Dependence of T cell antigen recognition on T cell receptor-peptide MHC confinement time

Abstract

T cell receptor (TCR) binding to diverse peptide-major histocompatibility complex (pMHC) ligands results in various degrees of T cell activation. Here we analyze which binding properties of the TCR-pMHC interaction are responsible for this variation in pMHC activation potency. We have analyzed activation of the 1G4 cytotoxic T lymphocyte clone by cognate pMHC variants and performed thorough correlation analysis of T cell activation with 1G4 TCR-pMHC binding properties measured in solution. We found that both the on rate (k(on)) and off rate (k(off)) contribute to activation potency. Based on our results, we propose a model in which rapid TCR rebinding to the same pMHC after chemical dissociation increases the effective half-life or "confinement time" of a TCR-pMHC interaction. This confinement time model clarifies the role of k(on) in T cell activation and reconciles apparently contradictory reports on the role of TCR-pMHC binding kinetics and affinity in T cell activation.

Figure 1

Binding Properties of 1G4 TCR Interaction with pMHC Variants Binding affinity and kinetics of 1G4 TCR interaction with a range of ESO-9V-HLA-A2 variants was measured at 37°C by SPR (see Figure S1). Effect of peptide or HLA-A2 amino acid substitution on 1G4 TCR binding affinity and kinetics is presented as a fold difference compared to ESO-9V-HLA-A2. Substitutions that negatively affected 1G4 TCR binding have relative affinities and kinetics lower than 1. Values shown are the mean ± SEM of at least three independently performed experiments.

Figure 2

Functional Response of 1G4 T Cells to pMHC Variants (A–C) IFN-γ release from 1G4 CTLs stimulated by plate-bound pMHC variants. 1G4 CTLs were cultured for 4 hr in 96-well plates coated with indicated concentrations of pMHC variants. Supernatants were collected and secreted IFN-γ was measured by ELISA. One of at least three independently performed experiments is shown. (D–E) Cytotoxic response of 1G4 CTLs to NY-ESO_157-165 peptide variants. A 1:1 ratio of 1G4 CTLs and ⁵¹Cr-labeled T2 cells were pulsed with the indicated peptide dilutions starting from the concentration shown in previous experiments (Figure S2D) to give a 40% increase in surface HLA-A2 expression (C_40%) on T2 cells. After 4 hr incubation at 37°C, specific lysis of T2 cells was analyzed by measuring released ⁵¹Cr. One of at least three independent experiments is shown.

Figure 3

Correlation between T Cell Response and TCR-pMHC Binding Properties Correlations are shown between pMHC potency, represented by the concentration stimulating half-maximal IFN-γ secretion (EC₅₀), and (A) the TCR-pMHC bond dissociation constant (K_D), (B) the bond off rate (k_off), (C) the bond on rate (k_on), (D) the change in enthalpy (ΔH), and (E) activation enthalpy of dissociation (ΔH^‡_diss). Table 1 contains correlations with additional thermodynamic parameters. Significant correlations (p < 0.05) are found only for K_D, k_off, and k_on and other parameters that are directly derived from these. The R² statistic is the square of the correlation coefficient for these simple models and p values are computed with an F-test. Both quantities are calculated in Matlab with standard methods; see Supplemental Information for details.

Figure 4

Analysis of Model Fitting to Data Subsets with Larger Variability in the On Rate (A) The 17 pMHC variants that comprise the entire data set exhibit a large correlation between K_D and k_off (R² = 0.74). By systematically removing a fixed number of pMHC variants, we identified the subset that minimized the K_D-k_off correlation. For example, we calculated the correlation for all 17 possible subsets of 16 pMHC variants and selected the subset with the smallest K_D-k_off correlation. (B) The square of the K_D-k_off correlation as a function of the number of pMHC variants removed for the subset with the smallest correlation. In this way, we generated data sets with lower K_D-k_off correlations and hence larger variability in the on rate. Also shown in (B) is the R² statistic for fits of the K_D, k_off, molecular flexibility (MF), confinement time (CT), and the combined model (MF+CT) to all subsets considered. Computations for subsets consisting of less than eight pMHC variants did not provide reliable results because the number of parameters approached the number of data points. (C and D) The correlations of pMHC potency with K_D and k_off for the subset of 13 pMHC variants (i.e., when 4 pMHC variants are removed). See Figure 5 for correlations with other models. In Table 2 we report the p values associated with each fit, showing that when removing more than two pMHC variants, correlations with K_D and k_off are not significant (p > 0.05). Also shown in Table 2 are p values associated with an F-test that determines whether the additional parameter(s) in the CT, MF, and combined model is significant. The fitted parameters from each model are listed in Table S2.

Figure 5

Correlation of pMHC Potency with Effective Off Rates (A) Model of TCR-pMHC confinement by rebinding. We used a mathematical model that accounted for a TCR-pMHC bound state (left), unbound but in physical proximity state (center), and a state where TCR and pMHC have moved apart (right). The rate of chemical dissociation is k_off and the rate of association is k^∗_on that is a first order rate, in units of s⁻¹, that depends on the macroscopic on rate k_on. When chemically dissociated (center), TCR-pMHC may move apart via diffusion or transport with a first order rate k₋. We propose that the potency of pMHC is governed by the amount of time it is confined to the TCR. The effective off rate to go from bound (left) to complete dissociation (right), given that potentially many rebinding events may take place, is given by k^∗_off = (k₋k_off)/(k^∗_on + k₋). (B–G) The panels show fits of the confinement time (CT), molecular flexibility (MF), and the combined (CT+MF) models to the entire data set (B, D, F) or the subset of 13 pMHC variants (C, E, G). The abscissa represents the effective off rate from each model. In the case of the confinement time model, it is (b₂k_off)/(k_on + b₂), where b₂ is a fitted parameter. In the case of the molecular flexibility model, the abscissa is k_offexp(b₂ΔC_p), where b₂ is a fitted parameter. See Experimental Procedures for details on data fitting and representation of effective off rates.