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The Journal of Immunology, 2007, 179, 2952 -2960
Copyright © 2007 by The American Association of Immunologists, Inc.
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CD8+ T Cell Activation Is Governed by TCR-Peptide/MHC Affinity, Not Dissociation Rate1

Shaomin Tian*, Robert Maile*,{dagger}, Edward J. Collins*,{ddagger} and Jeffrey A. Frelinger2,*

Departments of * Microbiology and Immunology, {dagger} Surgery, and {ddagger} Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
Binding of peptide/MHC (pMHC) complexes by TCR initiates T cell activation. Despite long interest, the exact relationship between the biochemistry of TCR/pMHC interaction (particularly TCR affinity or ligand off-rate) and T cell responses remains unresolved, because the number of complexes examined in each independent system has been too small to draw a definitive conclusion. To test the current models of T cell activation, we have analyzed the interactions between the mouse P14 TCR and a set of altered peptides based on the lymphocytic choriomeningitis virus epitope gp33–41 sequence bound to mouse class I MHC Db. pMHC binding, TCR-binding characteristics, CD8+ T cell cytotoxicity, and IFN-{gamma} production were measured for the peptides. We found affinity correlated well with both cytotoxicity and IFN-{gamma} production. In contrast, no correlation was observed between any kinetic parameter of TCR-pMHC interaction and cytotoxicity or IFN-{gamma} production. This study strongly argues for an affinity threshold model of T cell activation.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
The specificity of T cell activation clearly depends on the interaction of peptide/MHC (pMHC)3 complexes and TCR. Other signals via coreceptors such as CD4 and CD8 or costimulatory interactions such as CD28 and CD80/CD86 appear to act as amplifiers that increase the magnitude and/or duration of the TCR signals, and do not act independently. Because this interaction is the decisive event that is required for all downstream events with respect to adaptive T cell immunity, there is significant interest in what aspects of the molecular interactions control the signal to the T cell. There is a large literature that describes different T cell outcomes based on strength of signal of the interaction between pMHC and TCR (1, 2, 3, 4, 5). Unfortunately, strength of signal has not been well defined. Many immunologists view either affinity or off-rate for TCR to pMHC equally as surrogates to strength of signal, even though a large body of literature shows that affinity and off-rate are not directly related in this system.

Currently, there are three major theories of how the biochemistry of pMHC interactions impact downstream immunological responses (6, 7, 8, 9, 10). A kinetic model has been proposed that states that T cell signaling is highly dependent on the dissociation rate of pMHC from TCR. In this model, TCR-pMHC complexes with slow dissociation rates send positive signals to T cell, whereas fast off-rates result in negative signaling (8, 9). This model explains the experimentally observed relationship between T cell function and dissociation rate of ligand from receptor in some reports (11, 12, 13, 14, 15, 16). A second model proposed by Lanzavecchia and colleagues (10, 17) suggests that there is an optimal time of engagement (dwell time) for pMHC/TCR interactions and this optimal dwell time allows for serial binding events. This model is based on the measurements of internalization of TCR after engagement of pMHC. Additional experimental evidence for this model has been found in which binding was estimated by pMHC class I tetramer staining (18). Finally, there is an affinity model, which says that activation of T cells is related to the number of receptors engaged. This idea is supported by observations that TCR-pMHC affinity correlates with thymic selection (19, 20). This model argues that high-affinity pMHC will occupy a larger number of TCRs and thus are able to trigger stronger T cell responses (6, 7, 21, 22, 23, 24, 25, 26).

These three models are all still debated because none of them explain all available experimental data. Many of the experimental results are based on multiple comparisons across different TCRs on different cell types, including T cell hybrids, mutated T cell receptors, and multiple ligands, without a consistent data set available for any individual pMHC ligand or TCR. This makes it difficult to generate a consistent picture of how TCR-pMHC interactions play a role in T cell activation. We have chosen a reductionist approach. In this study, we use a single TCR and multiple, closely related pMHC ligands. We greatly simplify the biological readout by using T cells from a TCR transgenic mouse, activated in a uniform manner.

Using this approach, we set out to test these models directly. We selected the TCR from the TCR transgenic mouse P14, which is reactive with Db bound to the peptide epitope KAVYNFATC (gp33) of the lymphocytic choriomeningitis virus (LCMV) glycoprotein. The same system has been used in many studies of thymic selection and mature T cell activation (20, 27, 28, 29, 30, 31, 32). To create a large number of ligands to test for recognition by P14 TCR, we had a library of 58 peptide variants synthesized. Because we are interested in correlating TCR interactions for pMHC, not interaction of peptide with MHC, we selected only those peptides that stably bound Db with long t1/2. We then measured the binding affinity and association and dissociation kinetics between P14 TCR and peptide/Db complexes using surface plasmon resonance (SPR). For our biological readout of T cell activation, we decided to use lysis of peptide-pulsed target cells because this is a major role of a cytotoxic T cell, and the cytokine IFN-{gamma} production induced by each peptide. CTL activity by preactivated T cells was chosen because it is the fastest biologically relevant readout and is less susceptible to pMHC stability. IFN-{gamma} was chosen because it is often viewed as an end effector function and represents a beginning to end model. Our data clearly show that both CTL recognition and IFN-{gamma} production correlate to TCR-binding affinity, but not t1/2, and argue for an affinity threshold model.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Disclosures
 References
 
Mice

P14 TCR-transgenic mice (B6;D2-Tg(TcrLCMV)327Sdz/JDvsJ) expressing a transgenic TCR specific for LCMV gp33 peptide (33, 34, 35, 36, 37, 38, 39, 40, 41) were purchased from The Jackson Laboratory and were backcrossed to N10 and then carried as homozygotes in our laboratory. All mice used in this study were maintained under specific pathogen-free conditions in the American Association of Laboratory Animal Care-accredited University of North Carolina Department of Laboratory Animal Medicine Facilities and were routinely used at 8–12 wk of age.

Peptides and Abs

Peptides were synthesized using F-moc chemistry by the Peptide Synthesis Facility (Microbiology and Immunology, University of North Carolina), or by GenScript. All peptides were purified by HPLC, and purity and identity were confirmed by mass spectroscopy. The anti-CD8{alpha} (53-6.7) mAb and FITC-conjugated anti-mouse IgG were purchased from BD Pharmingen. Anti-Db mAb 28.14.8 and anti-Cβ TCR mAb H57-597 were produced from hybridoma supernatant (American Type Culture Collection). H57-597 was further purified with protein G column (Sigma-Aldrich).

Preparation of peptide/Db complexes

Peptide/Db complexes were prepared, as described previously (35). Briefly, residues 2–274 of Db and murine β2-microglobulin were produced in Escherichia coli as inclusion bodies and folded in vitro. Peptide, solubilized β2-microglobulin, and Db H chain were rapidly diluted into folding buffer (100 mM Tris (pH 8.0), 400 mM L-arginine, 5 mM reduced glutathione, 0.5 mM oxidized glutathione, and protease inhibitors) at molar ratios of 10:2:1. The folding buffers were incubated at 5°C for 24–48 h and then concentrated using an ultrafiltration cell (Millipore). The peptide/Db complexes were purified by HPLC gel filtration chromatography (Superdex 200; Applied Biosystems). Purity of prepared peptide/Db complexes was determined by SDS-PAGE.

Preparation of soluble P14 TCR

cDNAs for the {alpha}- and β-chains of the P14 TCR were used to construct expression vectors. Residues 1–216 and 1–247 of the mature {alpha} and β P14 TCR chains, respectively, were cloned into the pLM1 vector (a gift from G. Verdine, Harvard University, Cambridge, MA) for expression in E. coli. To help the dimerization of {alpha}- and β-chains in the process of refolding, a leucine zipper was added to the C-termini of {alpha} and β, respectively, with the basic peptide (SAQLKKKLQALKKKNAQLKWKLQALKKKLAQ) to {alpha}-chain, and the acidic peptide (SAQLEKELQALEKENAQLEWEL QALEKELAQ) to β-chain. Cys183 in β-chain was mutated to serine to improve refolding. The expression of TCR ectodomains in E. coli and inclusion body preparation were previously described (36). Soluble P14 TCR was prepared, as described previously (36, 37), with modifications. Briefly, equimolar {alpha}- and β-chain inclusion bodies dissolved in 8 M urea were combined and brought to a total volume of 20 ml with 10 mM Tris, 6 M guanidine-HCl, and 2 mM DTT (pH 8.0). This solution was injected into refolding buffer (1.2 M L-arginine, 100 mM Tris (pH 8.0), 2 mM KCl, 2 mM CaCl2, 2 mM reduced glutathione, 2 mM oxidized glutathione, and 0.2 mM PMSF) in six aliquots successively at 3-h intervals to give a total protein concentration of 0.2 mg/ml. After the final protein injection, the solution was kept at 4°C for 24 h. Then it was dialyzed extensively against 50 mM Tris (pH 8.0) and 150 mM NaCl. After filtration, the solution was concentrated by Centricon 84 (MWCO 10,000) and injected onto a Superdex S-200 gel filtration column (26/60) (Amersham Biosciences) at 1 ml/min in TBS (pH 8.0). The correctly folded TCR eluted at a Mr of ~58 kDa. Fractions containing correctly folded TCR were concentrated and stored at –80°C in aliquots. The activity of soluble TCR was evaluated with class I MHC tetramer ELISAs, as described previously (38). Briefly, the anti-Cβ TCR mAb H57-597 was used to coat the wells of a 96-well ELISA plate at 2 µg/ml. Subsequently, various dilutions of soluble TCR were added to the plate, followed by staining of C9M/Db tetramers at 30 nM, which contain HRP-conjugated avidin (Sigma-Aldrich) for detection. Following the addition of 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) (a HRP substrate), the amount of pMHC-tetramer binding was observed at 405 nm using a SPECTRAmax 190 plate reader (Molecular Devices).

Peptide stability assay

Briefly, TAP-deficient T2/Db cells were incubated with 100 µM gp33 or altered peptide ligands (APLs) overnight at 37°C. Then cells were washed and incubated with brefeldin A at 10 µg/ml for 1 h at 37°C to block the exit of new class I molecules. Cells were transferred into RPMI 1640 (supplemented with 10% FBS) containing brefeldin A at 0.5 µg/ml and kept at 37°C. Aliquots of cells were taken out for immunostaining over time. Cells were first stained with anti-Db 28.14.8 hybridoma supernatant, then FITC-conjugated anti-mouse IgG. Mean fluorescence intensity was measured on a FACScan (BD Biosciences).

Cytotoxicity assay

Cytotoxic assays in this study were performed in a standard 4-h 51Cr release assay described previously (39). Briefly, splenocytes from P14 TCR-transgenic mice were isolated and stimulated with 10 µM C9M (a variant of gp33) at 3 x 105/well. After 48 h, the cells were pooled, washed, and used as effector cells. EL4 cells were labeled with 51Cr (PerkinElmer Life and Analytical Sciences), then pulsed with various concentrations of peptides, and used as target cells. An altered HY peptide (ACSRNRQYL) was used as negative control. Effectors and targets at an E:T ratio of 5 were incubated at 37°C in 5% CO2 for 4 h, and the supernatant was harvested. In some experiments, effector cells were incubated with CD8{alpha} blocking Ab (clone 53-6.7) at 5 µg/µl for >1 h, and then were added to target cells. 51Cr release was counted in a Cobra Auto gamma counter (Packard Instrument). Specific lysis was calculated, as previously described. Each data point represents the average of triplicate measurements.

IFN-{gamma} assay

Splenocytes from P14 transgenic mice were harvested and plated in 96-well plates at 3 x 105/well. Subsequently, various concentrations of peptides were added in. The supernatants were collected after 48-h incubation and then stored frozen at –20°C until assayed. IFN-{gamma} produced in the supernatants was quantified using mouse IFN-{gamma} ELISA kit from eBioscience with paired IFN-{gamma}-specific mAbs, according to the manufacturer’s recommended procedure.

SPR experiments

All SPR experiments were performed on a Biacore 2000 in the University of North Carolina Macromolecular Interactions Facility. H57-597 (anti-TCR Cβ mAb) in 10 mM NaAc (pH 5.0) was covalently bound to a Biacore CM5 sensor chip using standard amine coupling to generate 5,000–10,000 resonance units. Soluble P14 TCR (in HBS-EP buffer: 10 mM HEPES (pH 7.4), 150 mM NaCl, 3 mM EDTA, and 0.005% Tween 20) was then added at an appropriate concentration to generate 300–500 resonance units. Soluble class I MHC was injected onto the surface at a flow rate of 50 µl/min. Regeneration of surface was accomplished by removing TCR and MHC with 0.1 M glycine (pH 2.0). This procedure of P14 coupling, pMHC binding, and glycine regeneration was repeated for nine concentrations of pMHC (nine 2-fold dilutions, each in duplicate or triplicate) in random order. All binding experiments were performed at 25°C. Responses from a control channel with only H57-597 were subtracted from data at each concentration. Control injections of buffer were also subtracted to correct any possible slow dissociation of TCR from Abs over time. The data were then analyzed with Scrubber (University of Utah, Center for Biomolecular Interaction Analysis). The KD values were obtained from steady-state fitting of equilibrium-binding curves. The association and dissociation phases were fit using the single-site binding model of Clamp XP (40) (Center for Biomolecular Interaction Analysis, University of Utah) to obtain kon and koff values. In all cases of fitting, {chi}2 was below 1, residuals were small and random, and the experimental curves visually matched the predicted curves.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Disclosures
 References
 
Selection of peptides that stably bind to Db

To evaluate TCR-pMHC interactions and the potency of APL/MHCs to activate T cells, a library of 58 peptides (Table I) was synthesized with single substitutions at position 1, 2, 3, 4, 6, or 9 of the gp33 epitope, or double substitutions at position 1 and 9, 3 and 9, and 4 and 9. It is notable that most previous studies used mutant C9M as a surrogate to gp33 because of its stability to oxidation (27, 41, 42). Although the crystal structures showed no large difference between gp33/Db and C9M/Db (34, 42), our previous studies demonstrated that there is a large difference in T cell recognition of those peptides (34). Therefore, we used the original virus-encoded cysteine at position 9 for all peptides, except those with specified substitutions at this position.


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  		Table I. Peptide variants of gp33

 
We tested all of the peptides for their ability to stabilize Db on the cell surface using a pMHC stability assay, because peptides that do not bind class I stably are both poorly immunogenic and poorly recognized by T cells (32, 43). The t1/2 of pMHC dissociation was calculated from the loss of anti-Db reactivity as determined by flow cytometry. We found that 31 of the 58 peptides bound Db stably (12 of them were shown in Fig. 1; nonbinding peptides and peptides not further studied are not shown to improve clarity). Only peptides with t1/2 longer than 20 h were selected for further SPR analysis, and this peptide selection allows us to exclude peptide off-rate from Db as an important consideration in analyses that follow. For example, V3A is a poor stimulator for P14 T cells (44). Although V3A/Db complex binds to P14 TCR with high affinity (KD of 2.3 µM), V3A does not stabilize Db well (data not shown), so we excluded it for further biological function analysis because the biological activity would be a function of pMHC dissociation and rebinding as well as TCR-pMHC binding.


Figure 1
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  		FIGURE 1. Viral epitope gp33 and the APLs bind to Db efficiently. TAP-deficient T2/Db cells were incubated with 100 µM gp33 or APLs overnight at 37°C. Then cells were washed and incubated with brefeldin A at 37°C. At the indicated time points, cells were taken out and stained with anti-Db 28.14.8, followed by FITC-conjugated anti-mouse IgG. The mean fluorescence of cells was measured using a FACScan flow cytometer. No peptide control was included and expressed as percentage of gp33 fluorescence at time 0. The t1/2 of peptide dissociation was calculated by fitting the data to one phase exponential decay model (solid lines) using GraphPad Prism 4.01 for Windows. Data shown are representative of two to three independent experiments.

 
It is notable that although all peptides selected in this study bind Db stably, this does not suggest that peptide binding does not change the conformation of Db and thus significantly alters recognition and/or affinity to P14 TCR. Indeed, we expect that these changes directly alter the affinity of TCR/pMHC interaction. Previously, this was shown directly where substitution of the Lys at P1 with Ser caused significant changes in the molecular surface of the {alpha}1 domain in Db, an area P14 TCR most likely contacts (34).

The P14 TCR-gp33/Db interaction features low binding affinity and fast kinetics

We folded each of the peptides with E. coli-produced Db H chain and β2-microglobulin in vitro. Of the 31 peptides that stabilized Db on the cell surface, 14 were folded efficiently into complexes and isolated by gel filtration, confirming that these peptide/Db complexes are stable in solution.

SPR has proved to be a reliable method for measuring the affinity and kinetics of weak protein-protein interactions such as TCR-pMHC binding (1, 45). We measured the real-time binding of P14 TCR to these stable peptide/Db by SPR on a Biacore 2000 instrument. P14 TCR was noncovalently bound to a mAb (anti-Cβ H57-597) that was immobilized to the chip surface. In each measurement, the chip surface was regenerated and new TCR is injected to avoid loss of activity. The slight dissociation of the TCR from the Ab during measurement was corrected by subtracting a control injection of buffer. Fig. 2a shows the sensorgrams for TCR binding peptide/Db at various concentrations. The binding and dissociation data fit well to a one-site binding model using the global fitting program Clamp XP (40) (Fig. 2a). The differences in the curve fits and observed data are small and random (data not shown). The following kinetic data were calculated for gp33/Db-P14 TCR interaction: association rate (kon) is 1.33 x 105 M–1 s–1, and the dissociation rate (koff) is 1.35 s–1 (Table II). Steady-state dissociation constant (KD) of gp33/Db-P14 calculated from equilibrium-binding data at various pMHC concentrations is 12.2 µM (Fig. 2b and Table II). The binding affinity of P14 TCR to gp33/Db is within normal range for other TCR-pMHC I interactions reported (1, 46), but slightly lower than the previous report (2.3 µM) (44).


Figure 2
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  		FIGURE 2. P14 TCR binding to gp33/Db and APL/Db shows a variety of binding abilities. a, SPR sensorgrams for binding of pMHCs to P14 TCR. Anti-Cβ TCR mAb H57-597 was attached to a Biacore CM5 sensor chip and used to immobilize refolded P14 TCR. Using a Biacore 2000, various concentrations of pMHC proteins were flowed over the surface of the TCR-coated chip, and the response unit change was measured. Solid lines are experimental data of various concentrations of pMHC; dashed lines are a global fit of data using CLAMP XP (40 ). The highest concentrations of each pMHC are labeled, and the other lines are from 2-fold dilutions of the highest concentration. Each measurement was performed in duplicate or triplicate. b, Equilibrium binding of pMHCs to P14 TCR measured by the response of the Biacore instrument as a function of pMHC concentration. Solid lines represent nonlinear least square fit to a one-site binding using GraphPad Prism 4.01.

 

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  		Table II. Effects of gp33 variants on T cell cytotoxicity, IFN-{gamma} production, and TCR binding

 
As seen for gp33/Db, the binding of APL/Db complexes shows fast association and dissociation, although there are substantial variations among them (Fig. 2a and Table II). These complexes can be roughly divided into two groups. Db complexes with LCMV gp33 or four P9 single substitutions (C9F, C9L, C9M, and C9V) have similar on- and off-rates. This group of peptide/Db has high affinities, ranging from 8.6 to 12.2 µM (KD), and all of the peptide substitutions are large hydrophobic residues replacing the wild-type cysteine at position 9. The second group of APL/Db has lower on-rates, higher off-rates, and much lower affinities from 63 to 270 µM (KD) (Table II). It is worth noting that the dissociation constants obtained from kinetic fitting of association and dissociation phases (KD = koff/kon) are comparable to those derived from steady-state analysis for all complexes except K1MC9M/Db (Table II), indicating that the global fitting of the kinetic data with Clamp XP is appropriate. Impurities in pMHC are probably the cause of the relatively high nonspecific responses seen with the dissociation curves of K1SC9M/Db sensorgrams particularly at high concentrations of pMHC (400 µM) (Fig. 2a). The nonspecifically bound proteins elute from the chip at a slower rate than K1SC9M/Db does, which confounds determination of the off-rate (Fig. 2a). Similar high background nonspecific response has been observed previously by others (21). Removing the high concentrations of pMHC, however, has a very small effect on the rate constants determined. Because the fitting of association curves is good from which kon is derived to be 103 M–1 s–1 (Table II), together with dissociation constant obtained from steady-state analysis, koff was calculated to be 0.26 s–1, by koff = KD x kon. Nonspecific responses could also be from increasing aggregation of pMHC at such high concentration as 400 µM, which may influence kinetic measurements, although we have minimized the effect by running another gel filtration separation immediately before SPR experiments.

Besides the peptides shown in Table II, we have tested by SPR five other APL/Db complexes (K1A, K1S, K1N, K1Y, K1AC9M) (data not shown). Unfortunately, little specific interaction with P14 TCR was observed up to 400 µM pMHC for these pMHC, so we excluded them from the analysis. Instead, we included in our further biological function assays three double mutation peptides (V3LC9M, Y4FC9M, and Y4SC9M) that have SPR data reported previously (42).

Peptide mutations alter CTL response

To examine how these peptides with different binding parameters affect the activity of T cells, we chose to compare the recognition by cytotoxic T cell of peptide-pulsed target cells. This is a key function of activated CD8+ T cells, which can be measured over a wide range of concentrations. C9M-stimulated splenocytes from P14 transgenic mice were used as a uniform source of maximally activated effector cells in a 4-h 51Cr release assay. Target cells were preincubated with different concentrations of peptide. The concentration of peptide that generates 50% of the maximum lysis (EC50CTL) was calculated for each peptide to compare the response of different peptides. Parental peptide gp33 was included in all assays as a positive control. Fig. 3 shows one of three to five sets of data for each peptide, and averaged EC50CTL values are shown in Table II. These 12 peptides tested showed marked differences in their ability to sensitize target cells to killing by CD8+ T cell using the identical TCR (Fig. 3). The EC50CTL values derived from the data in Fig. 3 are in a nearly 5-log range from 0.53 nM (C9F) to >50 µM (K1SC9M) (Table II). Target cells pulsed with peptide mutants C9F, C9L, C9M, C9V, or K1MC9M were recognized more efficiently than virus-encoded gp33, whereas K1R and Y4A were recognized less efficiently. Only very weak CTL-mediated lysis was observed for K1SC9M, V3LC9M, Y4FC9M, and Y4SC9M at even 50 µM peptide concentration.


Figure 3
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  		FIGURE 3. Viral epitope gp33 and APLs variably sensitize P14 T cells to kill target cells. EL4 cells labeled with 51Cr and pulsed with peptides at various concentrations were incubated with activated splenocytes from P14 TCR transgenic mice (E:T ratio = 5) for 4 h. Specific lysis of EL4 cells was measured by 51Cr release. Irrelevant HY(K1A) peptide (ACSRNRQYL) was used as negative control. Data are shown as mean ± SD (n = 3), and are representative of three to five independent experiments. Using GraphPad Prism, data were fit into sigmoidal dose-response model to obtain EC50CTL values (shown in Table II) (fitting curves are not shown).

 
Altered peptides varied in ability to induce IFN-{gamma} production

To determine the effect of mutations on the activation of naive T cells, we examined the ability of these 12 peptides to induce cytokine IFN-{gamma} production. The splenocytes from naive P14 transgenic mice were incubated with various concentrations of peptide for 48 h at 37°C, and then the secreted IFN-{gamma} in the culture supernatants was detected by ELISA. Fig. 4 shows the titration curves for all peptides, in which the data were fitted to a sigmoidal dose-response equation. The concentration of peptide that generates 50% of the maximum amount of IFN-{gamma} (EC50IFN) was calculated for each peptide to compare the response of different peptides. Similar to the CTL response, the amount of IFN-{gamma} induced by these 12 peptides was also variable. The EC50IFN values derived from the data in Fig. 4 are in a range of ~3.5 logs from 63 nM (gp33) to >269 µM (Y4SC9M) (Table II), although not as wide as in CTL response. K1MC9M and gp33 are among the most efficient peptides, followed by C9F, C9L, C9M, and C9V, whereas V3LC9M and Y4SC9M are with the lowest efficiency inducing IFN-{gamma}.


Figure 4
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  		FIGURE 4. Viral epitope gp33 and APLs variably sensitize P14 T cells to produce IFN-{gamma}. Splenocytes from P14 transgenic mice were incubated with various concentrations of peptides for 48 h at 37°C. IFN-{gamma} secreted into the supernatant was quantified using mouse IFN-{gamma} ELISA kit with paired IFN-{gamma}-specific mAbs. Data are shown as mean ± SD (n = 3), and are representative of three to four independent experiments. The solid lines are the fitting of data into sigmoidal dose-response model using GraphPad Prism, with derived EC50IFN values shown in Table II.

 
Binding affinity correlates with biological functions

With these data in hand, we examined the correlation between CTL recognition/IFN-{gamma} production and binding parameters. When the Log EC50CTL determined from the CTL assay was plotted vs log KD, as shown in Fig. 5a, the data fit well into a Boltzmann sigmoidal equation (R2 = 0.84). This implies the higher binding affinity corresponds to the better CTL recognition, but with definite thresholds. In contrast, no apparent correlation was obtained between log EC50CTL and off-rate (or t1/2) of TCR-pMHC (Fig. 5c), as would be expected in any kinetic model. The best fitting (linear fitting) among all the equations that have been tried only returns a R2 = 0.002. Interestingly, in IFN-{gamma} production, the relationship between log EC50IFN and log KD showed a similar, but steeper sigmoidal curve (Fig. 5b) to that between log EC50CTL and log KD, although R2 was not as high (0.73). Again, no apparent correlation was observed between log EC50IFN and off-rate of TCR-pMHC interactions (Fig. 5d), indicating that kinetic model does not fit these data. Similarly, there was no good correlation between on-rate and log EC50CTL or log EC50IFN (data not shown).


Figure 5
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  		FIGURE 5. CTL response and IFN-{gamma} production are correlated with binding affinity of peptide/Db to P14 TCR. a and b, Correlations between log EC50CTL and log KD (a), log EC50IFN and log KD (b). All the data points are shown in Table II. The EC50CTL and EC50IFN values are in unit of M, and KD in µM. The solid lines are the fittings to Boltzmann sigmoidal equation as follows: Y = bottom + (top – bottom)/(1 + exp((V50 – X)/slope)), using GraphPad Prism. The KD values for V3LC9M, Y4FC9M, and Y4SC9M are adopted from a previous report (42 ). Note that the EC50CTL values for K1SC9M, V3LC9M, Y4FC9M, and Y4SC9M were shown on the graph as 50 µM, although the actual values could be higher than that, but are out of the measurable range with CTL assay. c and d, Correlations between log EC50CTL and koff (c), log EC50IFN and koff (d). The solid lines in c and d are linear fittings using GraphPad Prism 4.01. All of the data points are shown in Table II. The EC50CTL and EC50IFN values are in unit of M.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Disclosures
 References
 
To examine the models about how the biochemistry of TCR-pMHC interactions affects T cell recognition of pMHC complexes, we have synthesized a library of altered peptides of LCMV epitope gp33. A group of these peptides that bind Db stably was used to prepare recombinant peptide/Db complexes. Then the interactions between peptide/Db and recombinant P14 TCR were measured by SPR and were compared with their ability to sensitize T cells for cytolysis and cytokine production. Although most previous reports studying these models relied on one biological parameter, we chose to test two feature functions of activated CD8+ T cells: cytolysis of target cells by already activated T cells and IFN-{gamma} production by naive T cells, to better define whether T cell activation has any correlation with TCR/pMHC binding.

The 12 peptides used in this study are shown in Fig. 5a and include the wild-type epitope (gp33), 5 strong agonists (C9F, C9L, C9M, C9V, and K1MC9M), 2 weak agonists (K1R and Y4A), 1 weak partial agonist (K1SC9M) (34), 1 antagonist (Y4SC9M) (27), and 2 viral escape epitopes (V3LC9M and Y4FC9M) (47). The identification of new strong agonists from the group of P9 substitutions and a weak agonist from P1 substitutions supports our previous report that peptide termini of gp33 (K1 and C9) dynamically contribute to recognition of pMHC by P14 TCR (34).

The most potent peptide and least potent peptide tested in this study show almost 100,000-fold difference with respect to the EC50CTL values (5 x 10–5 to 5 x 10–10 M) in CTL recognition (Table II) and 5,200-fold difference in EC50IFN values in IFN-{gamma} production (2.6 x 10–4 to 5 x 10–8 M) (Table II). Previously, a 5- to 6-log range of CD8+ T cell activity measured by IL-2 production (SD50) has been reported (25). Moreover, the calculated binding energy of 7.0–4.38 kcal · mol–1, for the complexes studied in this work, falls into the range of binding energies for general TCR-pMHC interactions reported elsewhere (9.8–3.9 kcal · mol–1) (2). Hence, our panel of altered peptides is broad enough to be representative of all peptides and allows us to examine the various models of activation of CD8+ T cells.

Each of the three models described above makes different predictions about the shape of the affinity (or off-rate) vs activity curve. The kinetic model predicts a predictable relationship with off-rate. The slow off-rates should result in positive signaling, whereas a fast off-rate should result in negative signaling. We attempted to correlate off-rates with CD8+ T cell activities (both CTL response and IFN-{gamma} production); we found no significant correlation with either linear or nonlinear curve fits. The optimal engagement time model predicts a Gaussian distribution of off-rate vs activity. We cannot fit a Gaussian distribution to the data, either. In contrast, the affinity data in this study (Fig. 5, a and b) correlate best with a Boltzmann sigmoidal relation to both CTL response and IFN-{gamma} production (R2 = 0.84 and 0.73, respectively). Another TCR-pMHC system (AHIII-HLA-A2) also shows that the affinity constants correlate with activity better than do dissociation rates (our unpublished data). A similar correlation of affinity activity was implied by Holler and Kranz (25) using transfected hybridomas, but a sigmoidal curve was not seen in their system. In addition to cytolysis and cytokine production, T cell proliferation was recently reported to correlate to affinity (leaving out those peptides that do not stabilize MHC well) also using P14 TCR (44). Because the binding of P14 TCR and cognate pMHCs is very fast, to make sure that binding kinetics are accurate, we also tried a few other ways to fit the data, e.g., to fit just the rapid dissociation phase (the first 5 s) because slow dissociation phase is more affected by nonspecific binding, to fit without the highest concentrations in which aggregates might exist. The data (not shown) did not change the correlations shown in Fig. 5.

Our data clearly argue for an affinity model in which higher affinity generates more effective signaling. Furthermore, the existence of the sigmoidal correlation suggests that there is not only a low affinity threshold below which no T cell activation will be induced, but also an affinity ceiling above which no additional biological response will be achieved. A similar high-affinity threshold has also been described recently in clonal selection (26), as follows: all T cells bearing TCRs with affinities lower than this threshold abide by the rule of the higher the better, whereas those with affinity above this threshold expand without further selection. Together these findings indicate that TCR affinity matters greatly, but only within a narrow range. Three of these gp33 mutant complexes with very low affinities for P14 TCR have also been reported previously (V3LC9M, 264 µM; Y4FC9M, 681 µM; and Y4SC9M, 530 µM) (42). None of them is potent in cytolysis (Fig. 3), T cell proliferation, or virus clearance (27, 47, 48). These peptide mutants and K1SC9M with very low cytolytic activity and cytokine induction cluster closely, solidifying the lower portion of the sigmoidal curve. Our data reported in this study suggest the low- and high-affinity thresholds for P14 TCR agonist are probably ~300 and 5 µM, respectively.

Interestingly, different TCR-pMHC pairs may have different effective affinity ranges. Comparison of avidity by tetramer binding of P14 and HY TCRs suggests that wild-type recognition by the HY TCR is lower than that would be recognized by the P14 TCR (49, 50). This suggests other factors may set the threshold dynamically during development. Obvious mechanisms include the amount of TCR or CD8 (50), lowered levels of CD5, and increased intracellular concentrations of early signaling molecules such as Lck. Any of these might act as a rheostat to tune the threshold for activation and move the entire window to higher or lower intrinsic affinities.

Holmberg et al. (32) has studied the correlation between avidities/t1/2 of another group of gp33 APLs and T cell activity by pMHC tetramer staining, monomer competition binding, and tetramer decay assays. Their data suggest that both avidity and t1/2 correlate to TCR down-regulation, but are suspect because of the use of tetramers to estimate the affinity. It is impossible to predict quantitatively how the avidity of a tetramer affects binding, especially in a monomer competition-binding experiment as they performed (32). Their use of whole Ab as competitor in tetramer decay assay to measure off-rates can also result in problematic data (51). We think that tetramers may be used to determine qualitative information (and rank order), but should not be used to determine absolute quantitative relationships.

In this study, we discuss in detail how our data fail to support either kinetic or optimum dwell time models. First, no general correlation was seen for the two T cell functions and kinetics. Forcing a best-fit linear model into the kinetic data results in R2 values of <0.04 (Fig. 5). Other models fare worse and will not even converge for nonlinear models. For example, Y4A/Db and gp33/Db have similar dissociation rates, but they result in almost 3 logs different EC50CTL values and >2 logs different EC50IFN values. K1R/Db and gp33/Db have 8x difference in dissociation rates, but not much difference in EC50CTL. Other pairs of pMHCs include K1SC9M/Db and K1R/Db, K1R/Db and Y4A/Db, etc. Obviously, there is no direct correlation between cytotoxicity or IFN-{gamma} production and kinetic data. Qi et al. (16) suggested that this is due to the difference in t1/2 of receptor/ligand in solution and in the cellular environment with large conformational changes upon binding, and can be corrected using their formula. Whether this will explain all challenging data to the function-dissociation rate model remains to be tested. We find it interesting that their data clearly show a good affinity-T cell response correlation even without any correction.

Second, P14 TCR-pMHCs tested in this study show fast kinetics that is at the high end of range of TCR-pMHC interactions (except K1SC9M), more comparable to those protein-protein interactions with equivalent affinities (1). Consequently, the interaction between P14 TCR and agonist/Db occurs over a narrow t1/2 frame of 0.1–1.0 s (Table II) (again, K1SC9M seems to be an exception). It is hard to reconcile these data with a kinetic model. How can gp33/Db with such a short t1/2 (0.51 s; Table II) fully activate CD8+ T cells, whereas some other TCRs on CD8+ T cells (OT-1, 2C, A6, etc.) only respond fully to pMHCs with much longer t1/2 (e.g., OT-1 TCR binds to SIINFEKL/Kb for 31.5 s) (21, 52, 53)? Assuming the {zeta}-chain phosphorylation requires same amount of time in the same type of T cells (CD8+ or CD4+ T cells), the kinetic windows for these T cells should be very similar regardless of the different TCRs they bear. Obviously, this is not true for our data.

Our model system also allows us to see the effect of CD8 engagement on activity. CD8 is a coreceptor on CTLs, which binds to class I molecules irrespective of the peptide bound (54). CD8 binding to pMHC increases the avidity between the surfaces of APCs and T cells, by stabilizing TCR-pMHC complex (55, 56), and recruiting p56lck tyrosine kinase and enhancing signal transduction (57). However, some observations have also suggested that if a TCR has a sufficiently high affinity for its ligand, it can function CD8 independently (25, 49, 58, 59, 60, 61). We also tested our strong agonists for their ability to kill targets in the presence of anti-CD8 Ab (data not shown). Our data using anti-CD8 Ab showed that T cell activation by pMHC with binding affinity for TCR as high as KD of 7 µM is still CD8 dependent. Recent studies using CD8-negative hybridomas bearing TCR mutants with very high affinity (KD in nanomolar range) suggest that there is a sharp affinity threshold of CD8-independent activity that occurs at approximately a KD of 3 µM (25, 58). Our peptides tested in this study all apparently fall into the affinity range of CD8-dependent T cell activation.

In summary, by directly measuring the binding affinity and kinetics of P14 TCR to a set of altered gp33 peptide ligands in complex with Db, and the binding-initiated biological responses, we have shown that CD8+ T cell activation is correlated with affinity in a certain affinity range, as shown by the sigmoidal correlation. There is no apparent correlation between T cell cytotoxicity or IFN-{gamma} production and binding kinetics. Our results strongly argue for affinity threshold model for T cell activation.


   Acknowledgments
 
We thank Katherine Midkiff and Michael Johnson for technical assistance with animal experiments. We also thank Dr. Yael Benhar and members of Collins and Frelinger laboratories for helpful discussions. We thank Dr. John Frelinger, University of Rochester, for critical reading of the manuscript.


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
References
 
The authors have no financial conflict of interest.


   Footnotes
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 This work was supported by National Institutes of Health Grants GM 67143 (to J.A.F.) and CA 92368 (to E.J.C.). Back

2 Address correspondence and reprint requests to Dr. Jeffrey A. Frelinger, Department of Microbiology and Immunology, University of North Carolina, CB# 7290, Chapel Hill, NC 27599-7290. E-mail address: jfrelin{at}med.unc.edu Back

3 Abbreviations used in this paper: pMHC, peptide/MHC; APL, altered peptide ligand; LCMV, lymphocytic choriomeningitis virus; SPR, surface plasmon resonance. Back

Received for publication July 21, 2006. Accepted for publication June 28, 2007.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
 References
 

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