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1.
2.
J Vis Exp ; (184)2022 06 06.
Article in English | MEDLINE | ID: mdl-35723488

ABSTRACT

Phosphorylation is a necessary posttranslational modification that regulates protein function and directs cell signaling outcomes. Current methods to measure protein phosphorylation cannot preserve the heterogeneity in phosphorylation across individual proteins. The single-molecule pull-down (SiMPull) assay was developed to investigate the composition of macromolecular complexes via immunoprecipitation of proteins on a glass coverslip followed by single-molecule imaging. The current technique is an adaptation of SiMPull that provides robust quantification of the phosphorylation state of full-length membrane receptors at the single-molecule level. Imaging thousands of individual receptors in this way allows for quantifying protein phosphorylation patterns. The present protocol details the optimized SiMPull procedure, from sample preparation to imaging. Optimization of glass preparation and antibody fixation protocols further enhances data quality. The current protocol provides code for the single-molecule data analysis that calculates the fraction of receptors phosphorylated within a sample. While this work focuses on phosphorylation of the epidermal growth factor receptor (EGFR), the protocol can be generalized to other membrane receptors and cytosolic signaling molecules.


Subject(s)
Single Molecule Imaging , Immunoprecipitation , Microscopy, Fluorescence/methods , Phosphorylation , Protein Binding , Single Molecule Imaging/methods
3.
Science ; 376(6595): 880-884, 2022 05 20.
Article in English | MEDLINE | ID: mdl-35587980

ABSTRACT

Systems immunology lacks a framework with which to derive theoretical understanding from high-dimensional datasets. We combined a robotic platform with machine learning to experimentally measure and theoretically model CD8+ T cell activation. High-dimensional cytokine dynamics could be compressed onto a low-dimensional latent space in an antigen-specific manner (so-called "antigen encoding"). We used antigen encoding to model and reconstruct patterns of T cell immune activation. The model delineated six classes of antigens eliciting distinct T cell responses. We generalized antigen encoding to multiple immune settings, including drug perturbations and activation of chimeric antigen receptor T cells. Such universal antigen encoding for T cell activation may enable further modeling of immune responses and their rational manipulation to optimize immunotherapies.


Subject(s)
Antigens , CD8-Positive T-Lymphocytes , Cytokines , Lymphocyte Activation , Models, Immunological , Antigens/immunology , CD8-Positive T-Lymphocytes/immunology , Humans , Immunotherapy , Machine Learning , Receptors, Antigen, T-Cell/metabolism
4.
Mol Biol Cell ; 31(7): 695-708, 2020 03 19.
Article in English | MEDLINE | ID: mdl-31913761

ABSTRACT

Differential epidermal growth factor receptor (EGFR) phosphorylation is thought to couple receptor activation to distinct signaling pathways. However, the molecular mechanisms responsible for biased signaling are unresolved due to a lack of insight into the phosphorylation patterns of full-length EGFR. We extended a single-molecule pull-down technique previously used to study protein-protein interactions to allow for robust measurement of receptor phosphorylation. We found that EGFR is predominantly phosphorylated at multiple sites, yet phosphorylation at specific tyrosines is variable and only a subset of receptors share phosphorylation at the same site, even with saturating ligand concentrations. We found distinct populations of receptors as soon as 1 min after ligand stimulation, indicating early diversification of function. To understand this heterogeneity, we developed a mathematical model. The model predicted that variations in phosphorylation are dependent on the abundances of signaling partners, while phosphorylation levels are dependent on dimer lifetimes. The predictions were confirmed in studies of cell lines with different expression levels of signaling partners, and in experiments comparing low- and high-affinity ligands and oncogenic EGFR mutants. These results reveal how ligand-regulated receptor dimerization dynamics and adaptor protein concentrations play critical roles in EGFR signaling.


Subject(s)
ErbB Receptors/metabolism , GRB2 Adaptor Protein/metabolism , Protein Multimerization , Adaptor Proteins, Signal Transducing/metabolism , Animals , CHO Cells , Cricetulus , ErbB Receptors/genetics , Kinetics , Models, Biological , Mutation/genetics , Phosphorylation , Phosphotyrosine/metabolism , Single Molecule Imaging
5.
Cell ; 171(3): 683-695.e18, 2017 Oct 19.
Article in English | MEDLINE | ID: mdl-28988771

ABSTRACT

Epidermal growth factor receptor (EGFR) regulates many crucial cellular programs, with seven different activating ligands shaping cell signaling in distinct ways. Using crystallography and other approaches, we show how the EGFR ligands epiregulin (EREG) and epigen (EPGN) stabilize different dimeric conformations of the EGFR extracellular region. As a consequence, EREG or EPGN induce less stable EGFR dimers than EGF-making them partial agonists of EGFR dimerization. Unexpectedly, this weakened dimerization elicits more sustained EGFR signaling than seen with EGF, provoking responses in breast cancer cells associated with differentiation rather than proliferation. Our results reveal how responses to different EGFR ligands are defined by receptor dimerization strength and signaling dynamics. These findings have broad implications for understanding receptor tyrosine kinase (RTK) signaling specificity. Our results also suggest parallels between partial and/or biased agonism in RTKs and G-protein-coupled receptors, as well as new therapeutic opportunities for correcting RTK signaling output.


Subject(s)
Epigen/chemistry , Epiregulin/chemistry , ErbB Receptors/chemistry , ErbB Receptors/metabolism , Crystallography, X-Ray , Epigen/metabolism , Epiregulin/metabolism , Fluorescence Resonance Energy Transfer , Humans , Kinetics , Ligands , Models, Molecular , Protein Multimerization
6.
Bull Math Biol ; 76(2): 314-34, 2014 Feb.
Article in English | MEDLINE | ID: mdl-24307084

ABSTRACT

In this work, we develop a detailed, stochastic, dynamical model for the tryptophan operon of E. coli, and estimate all of the model parameters from reported experimental data. We further employ the model to study the system performance, considering the amount of biochemical noise in the trp level, the system rise time after a nutritional shift, and the amount of repressor molecules necessary to maintain an adequate level of repression, as indicators of the system performance regime. We demonstrate that the level of cooperativity between repressor molecules bound to the first two operators in the trp promoter affects all of the above enlisted performance characteristics. Moreover, the cooperativity level found in the wild-type bacterial strain optimizes a cost-benefit function involving low biochemical noise in the tryptophan level, short rise time after a nutritional shift, and low number of regulatory molecules.


Subject(s)
Escherichia coli/genetics , Escherichia coli/metabolism , Models, Biological , Operon , Tryptophan/genetics , Tryptophan/metabolism , Gene Expression , Genes, Bacterial , Kinetics , Mathematical Concepts , Models, Genetic , RNA, Bacterial/genetics , RNA, Bacterial/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , Stochastic Processes
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