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Computational Model of Chimeric Antigen Receptors Explains Site-Specific Phosphorylation Kinetics - PubMed

  • ️Mon Jan 01 2018

Computational Model of Chimeric Antigen Receptors Explains Site-Specific Phosphorylation Kinetics

Jennifer A Rohrs et al. Biophys J. 2018.

Abstract

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phosphoproteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the 10 tyrosine sites on the CD28-CD3ζ CAR is phosphorylated by lymphocyte-specific protein-tyrosine kinase (LCK) with distinct kinetics. The addition of CD28 at the N-terminal of CD3ζ increases the overall rate of CD3ζ phosphorylation. Our computational model identifies that LCK phosphorylates CD3ζ through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3ζ through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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Figures

Figure 1
Figure 1

CD3ζ sites are phosphorylated by LCK with different kinetics. (A) A schematic of the experimental liposomal system is shown. CD3ζ and LCK His-tagged proteins were purified and allowed to bind to large unilamellar liposomes bearing nickel-chelated lipids. Once proteins were bound, ATP was added, and the proteins were allowed to interact for various times before being subjected to phosphoproteomic mass spectrometry for quantification. (B) A sequence of CD3ζ intracellular domain is shown, with trypsin cut sites denoted. Individual ITAM tyrosine sites are labeled in different colors. Y64F indicates a tyrosine-to-phenylalanine mutation to ensure that each peptide only has one tyrosine phosphorylation site. This mutation does not influence overall phosphorylation kinetics (see Fig. S3). (C) Experimental data (circles) and sigmoidal fit (lines) for CD3ζ ITAM phosphorylation on liposomes containing 10% acidic POPS lipids are shown. Error bars represent the SD of two technical replicates normalized by site-specific standard curves. (D) The half-maximal time for each CD3ζ ITAM site is shown. Data represent mean and mean ± standard error of the fit to a four-parameter sigmoidal curve. (E) The Hill coefficient for each CD3ζ ITAM site is shown. Data represent mean and mean ± standard error of the fit to a four-parameter sigmoidal curve. To see this figure in color, go online.

Figure 2
Figure 2

A comparison of individual tyrosine site mutations and lipid concentrations on phosphorylation kinetics. Experimental data for each CD3ζ ITAM site for different experimental conditions are shown: wild-type (WT) 1 and 2 (biological replicates of CD3ζ with unmutated ITAMs on liposomes containing 10% POPS), XX mutant (mut) (where XX represents the tyrosine-to-phenylalanine ITAM mutation site for CD3ζ stimulated on 10% POPS liposomes), and X% POPS (where X represents the POPS concentration for liposomes bearing CD3ζ with wild-type ITAMs). Error bars represent the SD of two technical replicates normalized by site-specific standard curves. To see this figure in color, go online.

Figure 3
Figure 3

A comparison of CD3ζ phosphorylation mechanisms. A model analysis for mechanisms of (A) sequential-order phosphorylation, (B) random-order phosphorylation, (C) phosphate-priming phosphorylation (Phos) rates, and (D) competitive inhibition (Inhib) by unphosphorylated and phosphorylated CD3ζ ITAM sites is shown. (Top) The model was fitted to experimental data. Error represents the residual error between the model and the data for all data sets, including wild-type, individual tyrosine-to-phenylalanine mutants, and different liposome concentrations. AIC represents the Akaike information criterion calculation for each model (AICi). We also report the AIC difference (Δi) between each model and the model with the lowest AIC, as a means of model comparison. (Middle) Half-maximal times of the model predictions are shown. (Bottom) Hill coefficients of the model predictions are shown. Black dots indicate the mean values from the sigmoidal fit to the data shown in Fig. 1, D and F. To see this figure in color, go online.

Figure 4
Figure 4

Estimated parameter sets. Solid bars show the mean and SD of the 50 best fitted parameter sets. Shaded bars show the value of parameters that were held constant during fitting. To see this figure in color, go online.

Figure 5
Figure 5

A model comparison to literature data of CD3ζ phosphorylation and dephosphorylation. (A) Predicted phosphorylation profiles for wild-type, single, and double ITAM mutant CD3ζ are shown. Mutated ITAMs are indicated by (x). The model was implemented using initial conditions described in the model from (18): 1 CD3z/μm2, 1000 LCK/μm2, and phosphatase concentrations between 10 and 100,000 molecules/μm2. (B) The Hill coefficient of the predicted phosphorylation response for each CD3ζ mutant is shown. (C) EC50 of the predicted phosphorylation response for each CD3ζ mutant is shown. To see this figure in color, go online.

Figure 6
Figure 6

CD28 influences CD3ζ phosphorylation (Phos) kinetics. (A) A schematic of the His-tagged CD28-CD3ζ recombinant protein is shown. (B) Experimental data (circles) and model fit (lines) for CD3z ITAM phosphorylation on wild-type CD28-CD3ζ, CD28-Y206F-CD3ζ, and CD28-Y209F-CD3ζ are shown. Error bars represent the SD of two technical replicates normalized by site-specific standard curves. (C) Estimated parameter sets are shown. Solid bars show the mean and SD of the 50 best fitted parameter sets. Shaded bars show the value of parameters that were held constant during fitting. For all model fits, the value of XI was held constant at 0.115, based on the best fitted value from the CD3ζ-only model fits. To see this figure in color, go online.

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