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Neier SC, Ferrer A, Wilton KM, Smith SEP, Kelcher AMH, Pavelko KD, Canfield JM, Davis TR, Stiles RJ, Chen Z, McCluskey J, Burrows SR, Rossjohn J, Hebrink DM, Carmona EM, Limper AH, Kappes DJ, Wettstein PJ, Johnson AJ, Pease LR, Daniels MA, Neuhauser C, Gil D, Schrum AG. The early proximal αβ TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network. Sci Immunol 2020; 4:4/32/eaal2201. [PMID: 30770409 DOI: 10.1126/sciimmunol.aal2201] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 01/17/2019] [Indexed: 12/18/2022]
Abstract
During αβ T cell development, T cell antigen receptor (TCR) engagement transduces biochemical signals through a protein-protein interaction (PPI) network that dictates dichotomous cell fate decisions. It remains unclear how signal specificity is communicated, instructing either positive selection to advance cell differentiation or death by negative selection. Early signal discrimination might occur by PPI signatures differing qualitatively (customized, unique PPI combinations for each signal), quantitatively (graded amounts of a single PPI series), or kinetically (speed of PPI pathway progression). Using a novel PPI network analysis, we found that early TCR-proximal signals distinguishing positive from negative selection appeared to be primarily quantitative in nature. Furthermore, the signal intensity of this PPI network was used to find an antigen dose that caused a classic negative selection ligand to induce positive selection of conventional αβ T cells, suggesting that the quantity of TCR triggering was sufficient to program selection outcome. Because previous work had suggested that positive selection might involve a qualitatively unique signal through CD3δ, we reexamined the block in positive selection observed in CD3δ0 mice. We found that CD3δ0 thymocytes were inhibited but capable of signaling positive selection, generating low numbers of MHC-dependent αβ T cells that expressed diverse TCR repertoires and participated in immune responses against infection. We conclude that the major role for CD3δ in positive selection is to quantitatively boost the signal for maximal generation of αβ T cells. Together, these data indicate that a quantitative network signaling mechanism through the early proximal TCR signalosome determines thymic selection outcome.
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Affiliation(s)
- Steven C Neier
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA.,Mayo Graduate School, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Alejandro Ferrer
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Katelynn M Wilton
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA.,Mayo Graduate School, Mayo Clinic College of Medicine, Rochester, MN, USA.,Medical Scientist Training Program, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Stephen E P Smith
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - April M H Kelcher
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA.,Mayo Graduate School, Mayo Clinic College of Medicine, Rochester, MN, USA.,Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Kevin D Pavelko
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Jenna M Canfield
- Molecular Pathogenesis and Therapeutics PhD Graduate Program, University of Missouri, Columbia, MO, USA
| | - Tessa R Davis
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Robert J Stiles
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Zhenjun Chen
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria 3010, Australia
| | - James McCluskey
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Scott R Burrows
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia.,School of Medicine, University of Queensland, Brisbane, Queensland 4006, Australia
| | - Jamie Rossjohn
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton, Victoria 3800, Australia.,Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff CF14 4XN, UK
| | - Deanne M Hebrink
- Thoracic Diseases Research Unit, Division of Pulmonary Critical Care and Internal Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Eva M Carmona
- Thoracic Diseases Research Unit, Division of Pulmonary Critical Care and Internal Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Andrew H Limper
- Thoracic Diseases Research Unit, Division of Pulmonary Critical Care and Internal Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Dietmar J Kappes
- Blood Cell Development and Cancer Keystone, Immune Cell Development and Host Defense Program, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Peter J Wettstein
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA.,Department of Surgery, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Aaron J Johnson
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA.,Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Larry R Pease
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Mark A Daniels
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA.,Department of Surgery, School of Medicine, University of Missouri, Columbia, MO, USA
| | | | - Diana Gil
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA. .,Department of Surgery, School of Medicine, University of Missouri, Columbia, MO, USA.,Department of Bioengineering, College of Engineering, University of Missouri, Columbia, MO, USA
| | - Adam G Schrum
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA. .,Department of Surgery, School of Medicine, University of Missouri, Columbia, MO, USA.,Department of Bioengineering, College of Engineering, University of Missouri, Columbia, MO, USA
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Brown EA, Neier SC, Neuhauser C, Schrum AG, Smith SEP. Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation. J Vis Exp 2019. [PMID: 31498315 DOI: 10.3791/60029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.
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Affiliation(s)
- Emily A Brown
- Center for Integrative Brain Research, Seattle Children's Research Institute; Graduate Program in Neuroscience, University of Washington
| | - Steven C Neier
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Department of Medicine, Harvard Medical School; Broad Institute of Harvard and MIT
| | | | - Adam G Schrum
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri; Department of Surgery, School of Medicine, University of Missouri; Department Bioengineering, College of Engineering, University of Missouri
| | - Stephen E P Smith
- Center for Integrative Brain Research, Seattle Children's Research Institute; Graduate Program in Neuroscience, University of Washington; Department of Pediatrics, University of Washington;
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Lautz JD, Brown EA, Williams VanSchoiack AA, Smith SEP. Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network. J Neurochem 2018; 146:540-559. [PMID: 29804286 PMCID: PMC6150823 DOI: 10.1111/jnc.14466] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 05/04/2018] [Accepted: 05/22/2018] [Indexed: 12/30/2022]
Abstract
Cells utilize dynamic, network-level rearrangements in highly interconnected protein interaction networks to transmit and integrate information from distinct signaling inputs. Despite the importance of protein interaction network dynamics, the organizational logic underlying information flow through these networks is not well understood. Previously, we developed the quantitative multiplex co-immunoprecipitation platform, which allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins in shared complexes. Here, we adapt quantitative multiplex co-immunoprecipitation to define the activity-dependent dynamics of an 18-member protein interaction network in order to better understand the underlying principles governing glutamatergic signal transduction. We first establish that immunoprecipitation detected by flow cytometry can detect activity-dependent changes in two known protein-protein interactions (Homer1-mGluR5 and PSD-95-SynGAP). We next demonstrate that neuronal stimulation elicits a coordinated change in our targeted protein interaction network, characterized by the initial dissociation of Homer1 and SynGAP-containing complexes followed by increased associations among glutamate receptors and PSD-95. Finally, we show that stimulation of distinct glutamate receptor types results in different modular sets of protein interaction network rearrangements, and that cells activate both modules in order to integrate complex inputs. This analysis demonstrates that cells respond to distinct types of glutamatergic input by modulating different combinations of protein co-associations among a targeted network of proteins. Our data support a model of synaptic plasticity in which synaptic stimulation elicits dissociation of pre-existing multiprotein complexes, opening binding slots in scaffold proteins and allowing for the recruitment of additional glutamatergic receptors. Open Science: This manuscript was awarded with the Open Materials Badge. For more information see: https://cos.io/our-services/open-science-badges/.
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Affiliation(s)
- Jonathan D Lautz
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington, USA
| | - Emily A Brown
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington, USA
| | | | - Stephen E P Smith
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington, USA
- Department of Pediatrics, University of Washington, Seattle, Washington, USA
- Graduate Program in Neuroscience, University of Washington, Seattle, Washington, USA
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Smith SEP, Neier SC, Reed BK, Davis TR, Sinnwell JP, Eckel-Passow JE, Sciallis GF, Wieland CN, Torgerson RR, Gil D, Neuhauser C, Schrum AG. Multiplex matrix network analysis of protein complexes in the human TCR signalosome. Sci Signal 2016; 9:rs7. [PMID: 27485017 DOI: 10.1126/scisignal.aad7279] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposed surface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells, this system produced high-dimensional data that enabled visualization of network activity. A comprehensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchical clustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis, and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skin punch biopsies from control patients or patients with the autoimmune skin disease alopecia areata. This analysis distinguished disease patients from the controls, detected enhanced basal TCR signaling in the autoimmune patients, and identified a potential signaling network signature that may be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach that can provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies.
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Affiliation(s)
- Stephen E P Smith
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Steven C Neier
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Brendan K Reed
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Tessa R Davis
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Jason P Sinnwell
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Jeanette E Eckel-Passow
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | - Diana Gil
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Claudia Neuhauser
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Adam G Schrum
- Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
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