The CD8 T cell memory response to the HLA-A2-restricted influenza epitope M158–66 can be an instructive model of immune memory to a nonevolving epitope of a frequently encountered pathogen that undergoes clearance. This memory repertoire can be complex, composed of a large number of clonotypes represented at low copy numbers, while maintaining a focus on the use of VB17 T cell receptors with identified Ag recognition motifs. Such a repertoire structure might provide a panoply of clonotypes whose differential avidity for the epitope would allow responses under varying antigenic loads. This possibility was tested experimentally by characterizing the responding repertoire in vitro while varying influenza Ag concentration over five orders of magnitude. At higher and lower Ag concentrations there was increased cell death, yet a focused but diverse response could still be observed. Thus, one of the characteristics of complex memory repertoires is to provide effector function at extremes of Ag load, a characteristic that is not generally considered in vaccination development but may be important in measuring its efficacy.
The nature of robust T cell memory is still poorly understood despite its importance in responses to commonly occurring pathogens. Generation of effective memory T cell responses underlies successful vaccination. Although we have a much clearer understanding of how the immune system responds in primary and secondary responses (1, 2, 3), we have not yet identified the stages in development of immune responses to pathogens that are frequently re-encountered. Influenza virus represents a recurring pathogen that undergoes clearance. It is a useful model for analysis of recurring responses because one of the major epitopes for cytotoxic effector T cell function does not vary between all known infective strains of the virus (4).
One might expect that recurring exposure to the same epitope would result in an exquisite focusing of the memory repertoire on those T cell lineages (clonotypes) with the highest avidity for the epitope. Indeed, some studies of responses in mice have suggested that this would be the case (5, 6, 7); however, this may not be the general principle of immune reactivity. We have been studying CD8 T cell recall responses to the M1-derived epitope, M158–66, an immunodominant peptide derived from influenza A virus that induces a strong CD8 T cell response in practically all HLA-A2 individuals (8, 9). Early studies of CD8 T cell reactivity against virus-derived peptides concluded that M158–66 bound to HLA-A2.1 (M1-A2) is recognized preferentially by CD8 T cells expressing VB17 β-chains with an “IRSS” amino acid motif in the CDR3 (8, 9). Selection of M1-reactive T cells has been shown to occur in early childhood (10). Because of influenza infection early in life and continuing re-exposure, it would be logical to expect that the M1-specific memory TCR repertoire (M1 repertoire) in adults would be focused on only a few VB17 clonotypes with the IRSS sequence in the CDR3. However, our previous studies have shown that adult HLA-A2 individuals can possess a large number of M1-specific VB17 T cell clonotypes as defined by the CDR3β nucleotide and amino acid sequences (11). As expected, these clonotypes used the motifs previously identified, but their distribution was very interesting. In addition to a few higher frequency clonotypes, a large part of the repertoire was comprised of single-copy clonotypes (11). In-depth analysis of our experimental data led to the conclusion that the M1 repertoire is self-similar and fractal in nature (12). Since our original observation, such complex repertoires have also been reported for VB13-expressing CD8 T cells reacting against the mouse hepatitis virus S510 epitope (13, 14).
We questioned the significance of having complex repertoires to a single epitope. We hypothesize that repertoire complexity is a characteristic of robust immunological memory. A large pool of responding T cells, collectively, may efficiently and effectively respond to an infection because, separately, they are each optimized to respond to a distinct aspect of pathogen exposure, such as high or low viral load. We reason that a memory repertoire too focused on high- or low-avidity clonotypes would be nonresilient and often ineffective. For instance, high-avidity clonotypes could be driven into apoptosis via activation-induced cell death under conditions of rampant viral replication and high epitope concentrations. In contrast, a lower pathogen load or epitope concentration may go unnoticed by lower avidity clonotypes. To test the possibility that complex repertoires provide protection from different pathogen loads, we challenged an M1-reactive repertoire with different Ag doses. High and low Ag doses resulted in the death of a large portion of T cells, but even under these conditions there was a diverse number of clonotypes that responded. This study supports the hypothesis that robustness of T cell memory correlates with the polyclonal structure of TCR repertoires by providing clonotypes that can function in different pathogenic environments. Thus, the complexity of the responding T cell repertoire may be another measure of efficacy in future vaccination studies.
Materials and Methods
A healthy 47-year-old blood donor was typed as HLA-A2.1 using the Biotest SSP System (Biotest Diagnostics).
Generation of M158–66/HLA-A2.1-specific CD8 T cell cultures
PBMC were sorted with Miltenyi anti-CD8 microbeads (Miltenyi Biotec) according to the manufacturer’s recommendations. The purity of the CD8 T cell sort exceeded 95%. To generate CD8 T cell cultures, 2.5 × 105/ml sorted CD8 T cells were stimulated with 0.5 × 105/ml peptide-coated T2 cells (defective for TAP1 and TAP2 (15); 4 ml/well. Culture medium contained human rIL-2 (10 U/ml) and was supplemented with 14% supernatant from the IL-2-producing MLA 144 cell line (16). Before use, T2 cells were incubated overnight with various concentrations of M158–66 peptide (M1), irradiated (3000 rad), and the excess peptide was washed off before it was added to the cultures. Optimal culturing as indicated by T cell growth occurred with 1 μM peptide-loaded T2 cells. In the Ag concentration experiments, the concentration of peptide loaded on T2 cells varied by 10-fold dilutions from 100 to 0.001 μM. One micromolar concentration was found to be the optimum concentration for generation of Ag-specific T cell responses in vitro both for influenza A M1-specific responses and for EBV BMLF-1 specific to HLA-A2.1-restricted responses. At this concentration, the BMLF-1 clonotype repertoire was nearly identical between in vitro culture and freshly sorted BMLF-1-specific CD8 T cells directly ex vivo (data not shown). The CD8 T cells were cultured in 12-well plates and were refed with rIL-2-containing medium every 3–4 days and restimulated once a week with M1-loaded T2 cells. The cultures where T2 cells were not pulsed with peptide served as the controls.
TCR VB mAbs, M158–66/HLA-A2.1 tetramer and annexin V staining
To examine the TCR repertoire, CD8 T cells were sampled from peripheral blood or CD8 T cell lines, and then stained with a panel of VB family-specific mAbs (Immunotech) that defined 24 VB families. In order that the CD8 T cells were in a relatively rested state, the CD8 T cell lines were always sampled 7 days after the last peptide stimulation just before the next required restimulation to maintain the culture. Staining was accomplished per the manufacturer’s recommendations. In the cases where frequencies of M1-specific cells were assessed, cells were costained with VB-specific mAbs and M158–66/HLA-A2.1 tetramer (M1 tetramer) labeled with allophycocyanin (Beckman Coulter). The cells were stained at room temperature for 30 min and then washed in FACS buffer (PBS containing 2% FCS and 0.2% sodium azide). To quantify the number of apoptotic T cells, the CD8 T cell lines were stained with FITC-labeled annexin V for 15 min in annexin buffer according to the manufacturer’s recommendations (BD Biosciences). Flow cytometry was performed using a FACSVantage or FACSCalibur (BD Biosciences). Initially, the cells were gated on M1 tetramer, and then their distributions within diverse VB families were defined. To isolate M1-reactive cells, CD8 T cell culture samples were stained with either M1 tetramer and then FACS-sorted using a MoFlo Sorter (DakoCytomation). Purity of the FACS-sorted cells exceeded 97%.
M158–66 peptide and M158–66/HLA-A2.1 tetramer synthesis
The peptide M158–66 (GILGFVFTL) was synthesized on Pepsin KA resin (BioSource International) using a 9050 Pepsynthesizer (Millipore). Peptide was purified by reverse-phase HPLC (>90% purity) using a C18 column (Vydac). M158–66/HLA-A2.1 tetramer, labeled with allophycocyanin, were made at the University of Massachusetts Medical School Tetramer Core Facility described elsewhere (17).
RNA isolation, cDNA synthesis, and titration
CD8 T cells separated from peripheral blood, CTL cultures, or FACS-sorted with M1-A2 tetramer were used for RNA isolation and cDNA synthesis as described elsewhere (11). To have equal total ratio of TCR β-chain transcripts, cDNA samples were titrated using semiquantitative PCR amplification of the CB region as described elsewhere (18). Briefly, serially diluted cDNA aliquots have been amplified using forward and reverse CB-specific primers in nonsaturated PCR; then the amount of amplified cDNA products was quantified after visualization on resolving gels. Aliquots of cDNA samples, that contained an equal quantity of total TCR β-transcripts, were used for sequential CDR3β spectratype-generating PCR amplifications.
Detailed descriptions of CDR3β spectratyping, primer sequences and electrophoretic conditions were published previously (11, 19). Briefly, cDNA samples were amplified in a PCR mixture that contained two forward BV gene-specific and reverse primers, specific to BC gene and 5′ labeled with carboxyfluorescein. After 30 cycles of PCR amplification, 10 μl of VB-CB-amplified cDNA products was loaded and run off on 50% urea/5% polyacrylamide sequencing gels. This technique ensures CDR3β band visualization after gel scanning on the FluorImager 595 fluorescence detection system (Molecular Dynamics). Multiplex PCR amplification with two primer sets served as internal control of PCR and allowed testing of 24 VB families on a single gel. Multiple CDR3β bands reflect a clonal complexity within the VB family; where clones express β-chains of different lengths and band intensity indicates clone(s) dominance within a family (20).
PCR product cloning and sequencing
cDNA samples from M1-specific cultures were amplified with VB17- and unlabeled CB-specific primers in separate nonsaturated PCR and immediately subcloned into the plasmid vector pCR4-TOPO (Invitrogen Life Technologies). VB17-CDR3 plasmid subcloning is described elsewhere (11). One hundred to 200 plasmid subclones from each cDNA library have been sequenced using a Taq DyeDeoxy Terminator cycle sequencing kit (Applied Biosystems). CDR3 region junction sequences were identified and analyzed with IMGT/V-QUEST (38) and IMGT/Junction Analysis (39) (IMGT, International Immunogenetics Information System 〈http://imgt.cines.fr〉). Further analysis was performed using Lasergene (DNAstar) and MacVector (Accelrys). Analysis of VB17 and J region flanking sequences indicated <0.25% divergence from the genomic sequences, which can be attributed to the reverse transcriptase and AmpliTaq DNA polymerase infidelity. Only clonotypes containing VB17 CDR3β of 12 and 11 aa residues (21) were subject to statistical analysis. Clonotype nucleotide and amino acid sequences, and counting data are available.4
The M158–66-specific memory T cell repertoire
Although there are a number of surface markers that correlate with memory, we use the operational definition of those T cells that can respond in an in vitro recall response in a middle-aged individual as representing memory. For the studies presented here, we focused on the anti-M1 repertoire in an HLA-A2 individual using MHC tetramers, VB-specific mAbs, and clonotype analysis of unstimulated (ex vivo) and in vitro-stimulated cells. A clonotype is defined as the unique DNA sequence that results from TCR β-chain rearrangement.
The frequency of influenza A M1-specific CD8 T cells in the peripheral blood of the individual examined in this study was assessed by staining with M1-loaded HLA-A2 tetramers and found to be 0.20% of the CD8 T cell pool (Fig. 1⇓A, inset). As expected on the basis of previous work (8, 9, 11), VB17-expressing cells dominated in M1 epitope recognition. In this case they represented >45% of the CD8 TCR repertoire as assessed by costaining with M1-specific tetramer and VB mAbs (Fig. 1⇓A). This signifies a 10-fold increase in the percentage of VB17-expressing T cells in the M1-specific population when compared with the CD8 T cell population as a whole, where they represent only 3% (Fig. 1⇓B).
To study the clonal structure of the M1 repertoire at the molecular level, a larger number of cells was needed than can be obtained by simply sorting M1-specific cells directly from the peripheral blood. Therefore, in vitro analysis of the recall response of the peripheral cells, using the previously determined optimal antigenic peptide concentration of 1 μM, enhanced the frequency of Ag-specific cells 150-fold (Fig. 1⇑C). The frequency of the M1-specific cells increased from ∼0.20% directly ex vivo to 30% after five stimulations with peptide in culture and consistently maintained VB usage similar to that observed directly ex vivo (Fig. 1⇑, A and C). VB17-expressing cells dominated the M1-specific TCR repertoire, representing 90% of the response, but other VB families, such as VB7.1, 3, 20, 13.6, 21.3, 22, and 14, contributed to the response, as has been previously reported (22), although none of these represented >5% of the response (Fig. 1⇑, C and D).
Clonotype analysis was performed by cloning and sequencing the PCR products from either the total CD8 cells of an M1-specific T cell line or from a tetramer-positive sorted subpopulation. There were 82 clonotypes (Year 2002, Fig. 1⇑E) identified from the total CD8 cells in the culture and they showed a distribution similar to what we have reported earlier (Fig. 1⇑, F and G). The repertoire was dominated by many clonotypes observed at low frequency. When a rank-frequency plot was used to evaluate the data, the distribution of clonotypes could be described as power law-like (12). As described before, this complex data set can be defined by two parameters describing the power law-like distribution; the frequency of rank 1, and the rate with which the number of clonotypes decreases at higher ranks (Fig. 1⇑F, inset). The decay rate parameter is obtained by calculating the slope of the ln (rank) vs ln (rank frequency) (ln is a natural logarithm.) The value obtained, −2.06, is very similar to those described previously for another individual. A similar analysis of VB17+M1 tetramer-positive cells revealed a similar clonotype distribution (Fig. 1⇑G). Only 30 clonotypes were analyzed from the selected cells; however, of these, 16 were also observed in the total CD8 analysis, indicating a good concordance between the total and selected cultures. These analyses support our hypothesis in that the memory repertoire has a large number of clonotypes with most of them present at low frequencies.
Stability of the M158–66-specific memory T cell repertoire
If complex repertoires were associated with robust responses, we would expect that these complex power law-like distributions should be a stable characteristic of the M1 repertoire. We examined the repertoires of three additional blood samples from the same individual collected over 3 years and subjected them to the same analyses as described above. Similar use of VB17 was observed in the tetramer-positive population examined ex vivo as well as in Ag recall cultures (Fig. 1⇑E). The shape of the clonotype distributions were similar at each time point and could each be described as power law-like (Fig. 1⇑E). Although the general shape of the distribution was maintained at each time point, the identity of the clonotypes observed varied. As we have described previously, if a repertoire is fractal, then sampling at any one time point will be incomplete. We can estimate the level of clonotype identity at any time point by comparing the similarity between duplicate cultures analyzed from the same blood sample. Such a duplicate analysis was performed for 1 of the 2003 blood samples and 17 (20.2%) of 84 clonotypes analyzed were identical in both cultures.4 The overlap between any two longitudinal samples from the same individual was slightly lower than that observed between two samples from the same time. This could be in part due to the lower number of clonotypes analyzed at some of these time points (Fig. 1⇑E). Therefore, we conclude that the complex structure of the memory repertoire is maintained, but the stability of any one individual clonotype in the repertoire cannot be clearly determined using this sampling regimen. The stability of such a complex epitope-specific T cell repertoire over time would argue that this intricate clonotypic distribution is important for generating a robust recall response.
Responses under different peptide stimulation conditions
To test our hypothesis that complex repertoires are necessary to generate effective responses to a variety of Ag doses, we performed two experiments using peripheral blood from the same individual collected at two different time points, 6 mo apart. For in vitro recall responses, we found that pulsing APC with a 1 μM concentration of peptide yielded optimal Ag-specific T cell growth. In one experiment, we raised the Ag concentration, pulsing the APC with 1, 10, and 100 μM concentrations of M1 peptide. In a second experiment, we lowered the pulsing concentration from 1 μM down to a 0.001 μM concentration of M1 peptide using 10-fold dilutions. VB17 repertoire analysis was first estimated by spectratyping (Fig. 2⇓A), which showed that each culture was focused on the predominant CDR3 lengths typical of M1-specific T cells. The frequency of M1-specific cells in the CD8+ population of each culture was measured by tetramer staining (Fig. 2⇓B). The percentage of M1-specific cells decreased at the two extreme peptide concentrations. However, VB17+ cells (Fig. 2⇓C) predominated in all cultures, independent of the stimulating peptide concentration, and represented ∼90% of the M1 tetramer-positive population.
To estimate the T cell avidity for the M1 peptide, we assessed the mean fluorescence intensity (MFI)5 of M1 tetramer staining under the different culture conditions with varied peptide concentrations. Consistent with the concept that different Ag loads may select for M158–66-specific VB17 CD8 T cells with different avidity to the Ag, we observed an inverse relationship between the Ag concentration and intensity of tetramer staining. T cells responding to the lowest concentration of Ag showed the highest MFI of tetramer staining, while those stimulated with the highest concentration of Ag showed the lowest MFI (Fig. 2⇑D).
The clonotype analyses of the cultures showed the same distributional shape irrespective of the Ag dose (Fig. 3⇓). In experiment 1, the culture grown with the highest M1 peptide concentration (100 μM) had the lowest frequency of tetramer-positive cells, which limited our clonotypic analysis to 14 clonotypes but, nevertheless, fit a similar distribution profile whereby the majority of the culture was comprised of unique, single-copy clonotypes (Fig. 3⇓A). The percentage of unique clonotypes in the 100 μM M1-stimulated culture was higher (80%) than that in either the 10 or 1 μM M1-stimulated cultures (60%), consistent with the concept that clonotypes grown in the presence of the highest dose of peptide were highly adapted to this particular culture condition. In experiment 2, the lower range of peptide concentrations (1, 0.01, and 0.001 μM) used to stimulate M1-specific cultures all resulted in the same characteristic distribution of clonotypes that was observed in experiment 1 (Fig. 3⇓B). The percentage of clonotypes unique to each of the four lower dose cultures was similar (36–47%).
There were some differences in the MFI levels between experiments 1 and 2 that were most likely due to the fact that these lines were generated 6 mo apart. There are many factors that influence culture conditions affecting the growth of M1 tetramer-positive VB17 cells and also tetramer staining conditions in vitro from one time period to the next. Thus, we did not compare TCR repertoires responding to M1 epitopes at different densities from two different experiments; however, each experiment had a nominal 1 μM peptide-stimulated culture used as an internal control. VB17 mAbs and M1 tetramer MFI values and clonotype distributions as shown in Fig. 3⇑ are compared within each individual experiment. VB17 repertoires maintained their complex structure regardless of the peptide concentrations and time of blood draw. To control for this problem with variation in culture conditions and tetramer staining between experiments done at different times, we repeated the peptide concentration experiment using the full range of peptide concentrations at one single time point as shown in Fig. 4⇓.
In the cultures stimulated with a high concentration of peptide, we expected that activation-induced cell death would prevent the survival of high-avidity T cell clonotypes. At lower concentrations, we expected that a majority of the low-avidity T cell clonotypes would die due to a lack of activation. To test whether Ag load had an effect on T cell survival, we generated six cultures (experiment 3; Fig. 4⇑) from a single blood draw that were stimulated with the total range of peptide concentrations used in experiments 1 and 2 (Fig. 2⇑). As in the earlier experiments, the total number and the proportion of M1 tetramer-positive cells within the cultured lines (Fig. 4⇑, A and B) indicated that extremely high (100 μM) and low (0.001 μM) peptide concentrations were far from the optimum (1 μΜ) necessary to induce proliferation of M1-specific VB17 cells. VB17 cells still represented >90% of the M1 tetramer-positive population that survived in each of the cultures, regardless of the peptide concentration (Fig. 4⇑C). As in the earlier experiments, the MFI of the M1 tetramer staining increased with decreasing Ag concentration, which is consistent with the concept that a lower Ag dose supports the growth of T cells with increased avidity for the Ag (Fig. 4⇑D). To assess whether this change in MFI could be a result of M1-peptide induced TCR down-regulation during the culture period, we also examined the MFI of the VB17 mAbs bound to the M1 tetramer-positive CD8 T cells (Fig. 4⇑E). The MFI of the VB17 staining was similar between cultures stimulated with different peptide concentrations, except for the 1 μM culture, where the MFI was 2-fold higher (Fig. 4⇑E). At the lowest concentration of peptide, the MFI of VB17 staining was 50% lower than the 1 μM culture despite the fact that the MFI of M1 tetramer staining was 2-fold higher, which is consistent with these cells expressing T cell receptors with high avidity for the M1 peptide.
When the cells in the various cultures were stained with annexin V, the ratio of annexin-negative to -positive cells in the tetramer-positive pool decreased at both high and low peptide concentrations (Fig. 4⇑F). These observations are in keeping with the expectations stated above that cultures grown with a high concentration of peptide are enriched in lower avidity clonotypes and show higher levels of cell death relative to cultures grown with intermediate peptide concentrations. Cultures grown with a low peptide concentration are enriched in high-avidity clonotypes, but also show higher levels of cell death.
Our work on the “functional plasticity” of TCR repertoires has several important implications in our current understanding of how immune memory evolves over time and what would constitute effective T cell memory. It would be logical to expect that T cell clones expressing high-affinity TCRs are preserved in the memory compartment to provide efficient and effective pathogen clearance during a subsequent rechallenge. In fact, several studies conducted with animal models strongly support this notion (23, 24). However, recent studies would suggest that different Ag loads alter the quantity and quality of the T cell response and influence protective immunity (25, 26, 27). In this study, we demonstrate that Ag load can influence in vitro recall responses selecting T cells of different avidity, and that this is potentially mediated by apoptotic death of T cells upon seeing high or low doses of Ag. In this process, Ag load influences which pre-existing memory clonotypes are selected, and, interestingly, the T cell response continues to maintain a complex repertoire under all Ag conditions.
It is commonly assumed that the major mechanism for viral evasion is epitope modification that prevents memory T cell recognition but does not interfere with viral entry and replication. In this case, influenza A virus is very interesting since it expresses highly mutagenic (hemagglutin and neuraminadase) proteins (28, 29, 30) and highly conserved (M1) (4) protein, which contains highly immunogenic CD8 T cell epitopes (8, 9). For instance, CD8 T cell responses directed against M158–66 in HLA-A2 individuals exemplify a highly focused memory T cell repertoire wherein VB17 clones provide superb recall responses. However, included in this memory T cell repertoire are M1-specific cells expressing other TCR VB families. These “non-VB17”-expressing M1-specific cells, although effective in cytolytic assays, require almost two-log higher M1-A2 density to exhibit proliferation capacity equal to VB17+ cells (22). The frequency of M1-specific memory CD8 T cells has been reported to vary from 0.11 to 0.56% of the total CD8 T cell pool based on tetramer staining (31), or one cell per 1,500–52,000 PBMC based on ELISPOT analysis (32), taken as an indication that this is an immunodominant epitope-specific response. However, should antigenic shift occur between avian and human strains, resulting in the complete loss of B and CD4 T cell reactivity against the H5N1 strain (33), the immune response would rely heavily on the reactivity against conserved M1-derived epitopes. In such a scenario, our work would suggest that a high density of M158–66-HLA-A2.1 presentation during the initial phase of a recall response would cause activation-induced cell death of high-avidity VB17 clones. Under conditions where the predominant high-avidity VB17 clones are deleted, one might envision that non-VB17 clones, of presumably lower avidity, would be called upon to lead to an M1 response. However, due to their low precursor frequency, a response led by non-VB17 clones may be inefficient. Rather, based on the distinct and stable distribution profile of M1-specific VB17 clonotypes described here, it is more plausible that low-avidity VB17 cells, lacking the IRSS in the CDR3β motif might be recruited to the M1 response.
The purpose of the current study was to experimentally test whether the well-documented clonally complex M1-specific VB17 repertoire present in the T cell memory of healthy adults previously exposed to influenza A virus retained its ability to react against both extremes of M1-A2 epitope density without compromising its complex structure. This study demonstrates that complex repertoires are capable of supplying T cell clonotypes that can respond under varying peptide concentrations. Although we do not know exactly how the range of peptide concentrations added to the cultures correspond to the amount of Ag expressed on infected cells in vivo, they do represent a 2- to 3-log span of concentrations on either side of the in vitro optimum, which may reflect the in vivo optimum. Influenza virus clearance would require responses at both of these extremes, since, at peak infection, virus number per cell could be extremely high, whereas early or late in infection the number of epitopes per cell could be much lower. For example, Legge and Braciale (27) have recently demonstrated using primary influenza A infection in vivo, in mice, an inverse relationship between virus inoculum size and the magnitude of the subsequent CD8 T cell response. In their study the authors used influenza virus inoculums that varied in a range of 4 logs, resulting in early virus titers that also varied by 4 logs. High-dose influenza infection led to enhanced CD8 T cell apoptosis mediated by Fas ligand similar to activation-induced cell death and resulting in diminished CD8 T cells responses. One might expect that these tremendous variations in virus load and size of CD8 T cell responses would also impact on Ag-specific TCR repertoire development but this has yet to be determined.
Earlier studies of antiviral protection mediated by bulk CTL lines generated under conditions of extremely high or low epitope density revealed that the stimulating Ag dose influences the quality of the CTL induced (25, 26). In one of these studies, the investigators generated CTL lines from mice immunized with recombinant vaccinia virus expressing HIV-1 IIIB-derived gp160 (rVV-16) and then restimulated with autologous APC loaded in vitro with immunogenic peptide I10 where peptide concentrations varied among 100, 0.1 and 0.0001 μM (26). These lines demonstrated different functional avidity in cytotoxicity assays using target cells coated with different concentrations of the I10 peptide. The CTL line stimulated with the lowest peptide concentration (0.0001 μM) appeared to be of higher avidity, because it demonstrated >10-fold higher cytolytic activity than the cultured CTL line stimulated with the highest peptide concentration (100 μM stimulated). Interestingly, they included studies with a CTL culture stimulated with 1 μM soluble peptide (not loaded on an APC) left in the culture which was shown to induce the lowest avidity T cells, a technique that differed from ours. Furthermore, when the “high-avidity” CTL lines (0.0001 μM) were adoptively transferred into naive hosts concurrently challenged with rVV-16 the viral titer was nearly 1000-fold lower than in mice protected by either “low-avidity” (100 μM) or control CTL cultures. Their study also tried to address the issue of whether different peptide concentrations could effect TCR repertoire development. CD8 T cells in the low-avidity CTL cultures (100 μM) were represented mostly by the VB8 family (∼50%), while the proportion of VB8+ cells within the high-avidity CTL line (0.0001 μM) was three times lower (∼15%). However, since these studies were done before tetramers were used to define Ag-specific T cells, we do not know the frequency of Ag-specific cells present under the different culture conditions and whether the observed changes in repertoire represented Ag-specific cells or other bystander cells that were growing under those specific culture conditions. In our culture system, we observed that a much smaller fraction of the culture stimulated with the lowest Ag load was Ag-specific even though the Ag-specific cells were of high affinity. Therefore, there may have been a low number of high-avidity Ag-specific cells at the lowest peptide concentration used in the Alexander-Miller et al. (26) studies, and these may have composed the 15% of the population that expressed VB8. Also, as in our studies, their results demonstrated that the difference in T cell avidity between cultures was not due to changes in the level of TCR expression. Furthermore, they also noted that CD8 coreceptor expression did not change with culture conditions.
It is difficult to correlate viral epitope presentation in vivo to our in vitro systems, considering the complexity of viral-derived factors (replication kinetics, multiplicity of viral peptides presented by MHC, Ag processing) and those derived from the host (innate vs adaptive immunity, naive vs memory T cell repertoires, cytokine dependency). We would argue that even though our titration of peptide concentrations in vitro might not represent the “real” amount of M158–66 presented during an actual human influenza virus infection, our in vitro model did reveal that the clonally complex VB17 recall response is still capable of Ag recognition when viral epitope density varies 5 logs in magnitude. Interestingly, Alexander-Miller and colleagues (34) were able to recapitulate their original observation, made with in vitro cell lines, in vivo using viruses engineered with T cell epitopes that changed the avidity of the T cell response. Using paramyxovirus SV5, which almost exclusively induces a high-avidity T cell response against the P protein, they demonstrated that the avidity of the antiviral response in vivo could be altered by increasing the turnover of the P protein during viral replication through linkage to ubiquitin. Infection with a recombinant vaccinia virus (VV) expressing greater amounts of protein (VV-ubiquitin) elicited a significant increase in low-avidity cells compared with the more typical high-avidity response elicited by VV-P (34).
It should be noted also that our findings are important independent of viral load leading to higher levels of direct Ag presentation. One could envision other scenarios for which our data are highly relevant. For instance, if viral mutants arise this could impact processing or presentation of epitopes, or viral load may influence the amount of Ag that is presented potentially by cross-presentation vs direct presentation. Our in vitro model suggests that the clonally complex VB17 recall response is still capable of Ag recognition under any of these scenarios.
One might expect that recurring exposure to the same epitope would result in the focusing of the memory repertoire on those clonotypes with the highest avidity for the epitope. Although it is possible that there was even greater M1-specific repertoire diversity during the initial primary infection with influenza A virus, our data would suggest that even after multiple exposures to the virus, the M1-specific repertoire of a middle-aged individual still has significant flexibility and diversity. What might be the biological implications of harboring multiple epitope-specific clones of different avidities originating from the same or even different TCR VB families? The most plausible explanation, suggested by Berzofsky and colleagues (26) is that during the initial phase of viral infection, when the viral peptide-MHC level is relatively low, higher avidity cells are being activated. However, during the large-scale peak of viral replication, when high Ag burden would likely drive high-avidity cells to apoptosis, the low/intermediate-avidity cells would become activated. This theory is consistent with observations by Alexander-Miller (25) which demonstrated that during the peak of the VV infection high-avidity T cells predominated but lower avidity T cells survived into memory. This could result in the generation of a complex and stable memory TCR repertoire, as we have demonstrated here, with an enhanced capacity to respond to wide extremes of Ag load.
In addition to the role of complex repertoires in responding to varying Ag doses, repertoire complexity may have even broader implications in the enhancement of cross-reactive interactions to completely unrelated pathogens such as hepatitis C virus or EBV (35, 36). This would be an important area of future study. Repertoire complexity is generally not considered in vaccination, but has the potential to significantly impact the quality of T cell memory, indicating that measuring the complexity of the T cell response after vaccination should be seriously considered.
We thank Drs. R. M. Welsh and S. Stepp for their helpful discussions and K. Zelny for editorial assistance with the manuscript. We thank Veronique Giudicelli for assistance with batch use of IMGT/V-QUEST.
The authors have no financial conflict of interest.
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 Research Grants AI-49320 and DR-32520 and Immunology Training Grant 5 T32 AI-07349-16 and Worcester Foundation Grant.
↵2 The contents of this publication are solely the responsibility of the authors and do not represent the official view of the National Institutes of Health.
↵3 Address correspondence and reprint requests to Dr. Yuri N. Naumov, Department of Pathology, University of Massachusetts Medical School, Worcester, MA 01655. E-mail address:
↵4 The online version of this article contains supplemental material.
↵5 Abbreviations used in this paper: MFI, mean fluorescence intensity; VV, vaccinia virus.
- Received December 13, 2005.
- Accepted May 11, 2006.
- Copyright © 2006 by The American Association of Immunologists