In vivo CD8⁺ T cell control of immunodeficiency virus infection in humans and macaques

Abstract

Forty million people are estimated to be infected with HIV-1, and only a small fraction of those have access to life-prolonging antiretroviral treatment. As the epidemic grows there is an urgent need for effective therapeutic and prophylactic vaccines. Nonhuman primate models of immunodeficiency virus infection are essential for the preclinical evaluation of candidate vaccines. To interpret the results of these trials, comparative studies of the human and macaque immune responses are needed. Despite the widespread use of macaques to evaluate vaccines designed to elicit a CD8⁺ cytotoxic T lymphocyte (CTL) response, the efficiency with which CTL control immunodeficiency virus infections has not been compared between humans and macaques, largely because of difficulties in assaying the functional CTL response. We recently developed a method for estimating the rate at which CTLs kill cells productively infected with HIV-1 in humans in vivo. Here, using the same technique, we quantify the rate at which CTLs kill infected cells in macaque models of HIV infection. We show that CTLs kill productively infected cells significantly faster (P = 0.004) and that escape variants have significantly higher fitness costs (P = 0.003) in macaques compared with humans. These results suggest that it may be easier to elicit a protective CTL response in macaques than in humans and that vaccine studies conducted in macaques need to be interpreted accordingly.

Simian immunodeficiency virus (SIV) and simian–human immunodeficiency virus (SHIV) infection of nonhuman primates, notably rhesus macaques (Macaca mulatta) and pigtail macaques (Macaca nemestrina), reproduce many of the key elements of HIV infection of humans, including CD4⁺ cell depletion and an AIDS-like syndrome. Importantly, for appraising vaccine efficacy, macaques and humans have similar immune systems (1). However, it has been difficult to compare immune control of immunodeficiency viruses in humans and macaques. A number of commonly used SHIV strains (SHIV-89.6P, SHIV-DH12R, and SHIV-KU) are more susceptible to antibody neutralization than HIV-1, and it has therefore been suggested that they are poor systems for testing vaccines designed to elicit an antibody response (2). The efficiency with which CD8⁺ cytotoxic T lymphocytes (CTLs) control immunodeficiency virus infections has not been compared between humans and macaques, largely because of difficulties in assaying the functional CTL response (3). A method for quantifying CTL lytic function in vivo (4, 5) now makes this comparison possible.

Results

The rate at which a CTL escape variant grows out and replaces the wild-type (WT) strain, “the rate of escape,” is determined by the balance between the rate of CTL killing evaded by the escape variant and the fitness cost of the variant (see Methods). By quantifying the rate of escape and the fitness cost of known CTL escape variants the rate of killing of productively infected cells in vivo was estimated. For the purposes of analyzing the macaque CTL response we distinguished between vaccine-induced memory CTL responses and naturally occurring CTL responses. Vaccine-induced CTL responses were defined as responses in vaccinated macaques specific for the antigen introduced by vaccination. Natural CTL responses were defined as HIV-specific CTL responses occurring in naïve macaques or macaques vaccinated against a different protein. A comprehensive metaanalysis of all documented CTL escape events was performed; in total, 35 CTL escape variants were analyzed (6–17). Of these, 18 represented escape from a natural CTL response (12 occurred in naïve macaques, 6 in macaques vaccinated against a protein other than the one in which escape occurred) and 17 represented escape from a vaccine-induced memory CTL response. The references, challenge strains, and vaccination regimens are listed in Table 1. All published data sets were analyzed, and there was no reason to believe that the macaques we studied were not representative of the experimentally infected macaque population.

Table 1.

Rate of escape

Ref.	Macaque species	Challenge virus	CTL epitope*	Vaccinated	Regimen	Rate of escape, day−1	Standard error†
Escape from natural CTL responses
9	Rhesus	SIVppm‡	Nef 165–173	No	N/A	0.02	0.007
6	Rhesus	SIVsmE660	Env gp160 620–628	Yes	gag DNA	0.02	0.002
6	Rhesus	SIVsmE660	Gag 181–189	No	N/A	0.02	0.03
8	Rhesus	SHIV89.6P	Pol 584–592	Yes	IL-2 augmented gag/env DNA	0.06	0.001
10	Rhesus	SIVmac239/nef open	Tat 28–35	Yes	DNA prime/MVA boost gag p11C	0.12	0.03
10	Rhesus	SIVmac239/nef open	Tat 28–35	Yes	DNA prime/MVA boost gag p11C	0.13	0.04
11	Rhesus	Heterogeneous SIV	Nef 62–70	No	N/A	0.01	0.006
11	Rhesus	Heterogeneous SIV	Nef 136–146	No	N/A	0.04	0.001
9	Rhesus	SIVppm‡	Nef 165–173	No	N/A	≥0.04
6	Rhesus	SIVsmE660	Env gp160 620–628	Yes	gag DNA	≥0.01
6	Rhesus	SIVsmE660	Env gp160 620–628	Yes	gag DNA	≥0.01
6	Rhesus	SIVsmE660	Gag 181–189	No	N/A	≥0.02
6	Rhesus	SIVsmE660	Gag 181–189	No	N/A	≥0.02
6	Rhesus	SIVsmE660	Gag 181–189	No	N/A	≥0.01
6	Rhesus	SIVsmE660	Env gp160 620–628	No	N/A	≥0.01
6	Rhesus	SIVsmE660	Env gp160 620–628	No	N/A	≥0.02
17	Rhesus	SIVmac239/nef open	Nef 159–167	No	N/A	≥0.03
15	Rhesus	SIVmac251	Gag 181–189	No	N/A	≥0.03
Escape from vaccine-induced CTL responses
7	Rhesus	SIVmac239	Gag 206–216	Yes	DNA prime/SeV boost gag	0.33	0.01
7	Rhesus	SIVmac239	Gag 367–381	Yes	DNA prime/SeV boost gag	0.19
12	Pigtail	SHIVSF162P3	Gag 164–172	Yes	DNA prime/FPV boost gag	0.65	0.05
12	Pigtail	SHIVSF162P3	Gag 164–172	Yes	DNA prime/FPV boost gag	0.43	0.2
7	Rhesus	SIVmac239	Gag 206–216	Yes	DNA prime/FPV boost gag	≥0.61
7	Rhesus	SIVmac239	Gag 206–216	Yes	DNA prime/FPV boost gag	≥0.19
7	Rhesus	SIVmac239	Gag 50–65	Yes	DNA prime/FPV boost gag	≥0.31
16	Rhesus	SHIV89.6P	Gag 181–189	Yes	IL-2 augmented gag/env DNA	≥0.25
6	Rhesus	SIVsmE660	Gag 181–189	Yes	gag DNA	≥0.02
6	Rhesus	SIVsmE660	Gag 181–189	Yes	gag DNA	≥0.01
6	Rhesus	SIVsmE660	Gag 181–189	Yes	gag DNA	≥0.02
17	Rhesus	SIVmac239/nef open	Nef 159–167	Yes	DNA prime (all ORFs)/MVA boost (gag, pol, env, nef, rev, tat)	≥0.03
14	Rhesus	SIVmac251	Nef 211–225	Yes	Nef and Gag lipopeptides	≥0.02
14	Rhesus	SIVmac251	Nef 112–119	Yes	Nef and Gag lipopeptides	≥0.01
15	Rhesus	SIVmac251	Gag 181–189	Yes	NYVAC gag, pol, env	≥0.15
13	Rhesus	SIVmac251	Nef 128–137	Yes	Nef and Gag lipopeptides	≥0.03
13	Rhesus	SIVmac251	Nef 128–137	Yes	Nef and Gag lipopeptides	≥0.02

N/A, not applicable.

*All epitopes numbered relative to SMM239 http://hiv-web.lanl.gov/content/hiv-db/LOCATE_SEQ/locate.html.

^† Calculated using asymptotic covariance matrix method. For the 20th entry only two (non-zero, non-one) points available so SE could not be calculated.

^‡ Biological isolate of SIV_mac239.

In the majority (23/35) of cases of CTL escape considered, outgrowth of the CTL escape variant was so rapid that it was only possible to put a lower bound on the rate of escape (Fig. 1). The median lower bound on the rate of escape was 0.02 day⁻¹. Of the 12 escape events, which were slow enough or sampled frequently enough to make precise quantification possible, the median rate of escape was 0.09 day⁻¹ (Table 1). The corresponding escape rates in humans have been reported in ref. 4. In humans a smaller proportion of events (10/31) were too rapid to be precisely quantified, despite the fact that the average time between observations was longer in humans than in macaques. The median lower bound on the events that were too rapid to be captured by the sampling protocol was 0.01 day⁻¹; the median rate of escape of those events slow enough to be fully quantified was also 0.01 day⁻¹.

Fig. 1.

Estimating lower bounds on the rate of escape. If CTL escape is too rapid to be fully captured by the frequency of observations (i.e., there are less than two points when the fraction of sequences bearing the escape variant is not 0% or 100%) then the escape rate calculated (solid line) will be a lower bound on the escape rate. The true escape rate could be considerably faster (dashed line) but not slower. ■, experimental data points; solid line, slowest escape consistent with the data (gives estimated lower bound on the rate of escape); dashed line, faster escape, also consistent with the experimental data.

We wanted to compare the rate of CTL escape in humans with the rate of CTL escape in macaques. However, because a large number of escape events were too rapid to be quantified there was a strong negative correlation between the rate of escape and the average sampling interval (macaques P < 0.0001; human P < 0.0001; Spearman rank correlation two-tailed test). Consequently, it was necessary to compare estimates in humans and macaques while controlling for the length of the sampling intervals. This comparison was achieved by dividing the data set into four equally sized groups with similar average sampling intervals and then comparing human and macaque estimates within each group (see Methods). The results show that the rate of escape was significantly faster in macaques than in humans (Fig. 2). This result was true whether we considered escape from natural CTL responses (humans compared with naïve macaques and macaques vaccinated against a different protein; P = 0.004; Fisher's χ² two-tailed test); escape from natural CTL responses in naïve subjects only (humans compared with naïve macaques; P = 0.008; Fisher's χ² two-tailed test), or escape from all CTL responses (humans compared with naïve and vaccinated macaques; P = 0.00001; Fisher's χ² two-tailed test). We considered six alternative grouping strategies to ensure that the results were robust to the choice of strategy. For every grouping strategy, for all three comparisons, the rate of escape was significantly faster in macaques compared with humans despite the fact that the majority of the estimates in macaques were only lower bounds.

Fig. 2.

Rate of escape from natural CTL responses in humans and macaques. The rate of escape from a single, natural CTL response is plotted as a function of the mean time interval between observations. Data from humans are shown in black, and data from macaques are in gray. Estimates that are lower bounds are denoted by open symbols. The x axis is cropped at 350 days. ■, rate of escape in humans; □, lower bound on the rate of escape in humans; gray circle, rate of escape in macaques; ○, lower bound on the rate of escape in macaques. The rate of escape was significantly faster in macaques than in humans (P = 0.004; Fisher's χ² two-tailed test) despite the fact that the majority of estimates in macaques were only lower bounds. The lines are predicted values from an analysis of covariance with log-transformed escape rates as the dependent variable and time interval and species as the two independent variables (solid line, humans; dashed line, macaques).

One possible explanation for the difference between humans and macaques was that the viral escape rate was influenced by infection stage (acute/chronic) and that escape in the acute stage was observed more frequently in macaques than in humans. However, differences in disease stage did not appear to explain the observed differences in escape rate between humans and macaques because the difference in escape rates between macaques and humans remained statistically significant even if all of the chronic HIV data sets were excluded (P = 0.009; Fisher's χ² two-tailed test). Furthermore, there was no significant difference in the rate of escape in macaques between acute and chronic infection (P = 1; Fisher's χ² two-tailed test).

The rate at which a CTL escape variant outgrows and replaces the WT is determined by the balance between the strength of the CTL response evaded and the fitness cost incurred by the escape variant (4, 12). We found that CTL escape variants grew out significantly more rapidly in macaques than in humans. This finding indicates either that CTL responses to single epitopes are stronger in macaques and/or that escape variants in SIV and SHIV have lower fitness costs than escape variants in HIV (i.e., SIV and SHIV are more tolerant of the relevant mutations). To test whether the latter was the case we quantified the fitness costs of escape variants by estimating the rate of reversion of CTL escape variants to WT on transmission to macaques that did not possess the MHC class I allele necessary to present the relevant epitope. The rate of reversion (fitness cost of the escape variant compared with the WT) was quantified in nine cases (12, 18); in three cases reversion was so rapid that it was only possible to make a lower bound estimate (Fig. 3 and Table 2). The rate of reversion in macaques was compared with the rate of reversion in humans that had been previously quantified in seven cases, one of which was a lower bound (4). Because the majority of rates were precise estimates rather than lower bounds it was possible to compare the rates without correcting for the sampling interval (i.e., using a Wilcoxon–Mann–Whitney test). Surprisingly, the rate of reversion was significantly higher in macaques (P = 0.003 Wilcoxon–Mann–Whitney exact two-tailed test), not lower as expected.

Fig. 3.

Rate of reversion in humans and macaques. The fitness cost of a CTL escape variant is equal to the rate of reversion of the variant to the WT on transmission to a host who does not possess the MHC class I allele to bind the WT epitope. The rate of reversion was significantly faster in macaques (P = 0.003; Wilcoxon–Mann–Whitney two-tailed, exact); the difference was still significant even if the four fastest rates of reversion observed in macaques (which seem to form a separate group of outliers) were excluded (P = 0.048; Wilcoxon–Mann–Whitney two-tailed, exact). ■, rate of reversion in humans; □, lower bound on the rate of reversion in humans; gray circle, rate of reversion in macaques; ○, lower bound on the rate of reversion in macaques.

Table 2.

Rate of reversion

Ref.	Macaque species	Challenge virus	CTL epitope*	Vaccinated	Regimen	Rate of reversion, day−1	Standard error†
18	Rhesus	SIVsmE660 Variant	Gag 181–189	No	N/A	0.03	0.002
18	Rhesus	SIVsmE660 Variant	Gag 181–189	No	N/A	0.00	0
12	Pigtail	SHIVmn229	Gag 164–172	No	N/A	0.38	0.1
12	Pigtail	SHIVmn229	Gag 164–172	No	N/A	0.34	0.01
12	Pigtail	SHIVmn229	Gag 164–172	No	N/A	0.41	0.04
12	Pigtail	SHIVmn229	Gag 164–172	No	N/A	0.45	0.1
18	Rhesus	SIVsmE660 Variant	Gag 181–189	No	N/A	≥0.10
18	Rhesus	SIVsmE660 Variant	Gag 181–189	No	N/A	≥0.07
18	Rhesus	SIVsmE660 Variant	Gag 181–189	No	N/A	≥0.05

N/A, not applicable.

*All epitopes numbered relative to SMM239, http://hiv-web.lanl.gov/content/hiv-db/LOCATE_SEQ/locate.html.

^†Calculated by asymptotic covariance matrix method.

The rate at which a single CTL response (CTL clone or clones recognizing a single epitope) kills productively infected cells in vivo is equal to the sum of the escape rate and the reversion rate (see Methods). Both the rate of escape and the rate of reversion were significantly higher in macaques than in humans. This result indicates that the rate at which a CTL response to one epitope kills infected cells in vivo is significantly higher in macaques than in humans. The mean difference between macaques and humans in the rate of escape from a natural CTL response was 0.02 day⁻¹; the mean difference in the rate of reversion was 0.2 day⁻¹. The difference in the rate of killing of productively infected cells by a single epitope-specific CTL response between humans and macaques was therefore of the order of 0.22 day⁻¹ (0.02 + 0.2), which means that infected cells were killed almost 10 times more rapidly in macaques than in humans.

In general, there was no significant difference in the rate of escape from natural and vaccine-induced CTL responses (P = 0.12; Fisher's χ² two-tailed test). However, a wide range of vaccination strategies were used, including some that appeared to have little or no effect on CTL control, perhaps contributing to this negative result. Interestingly, heterologous prime-boost vaccination protocols, which are believed to be among the most promising vaccine strategies (19), induced CTL responses that were highly significantly stronger than those induced by other vaccination strategies (P < 0.0005; Wilcoxon–Mann–Whitney exact two-tailed test) or occurring naturally (P < 0.0005; Wilcoxon–Mann–Whitney exact two-tailed test). However, this observation needs to be treated cautiously as the sampling interval in the cases of heterologous prime-boost vaccination was shorter than in other cases, making a direct comparison of rates of escape problematic because the most appropriate statistical test, Fisher's χ², could not be performed.

Discussion

Our work implies that CTL responses in humans kill cells productively infected with immunodeficiency virus significantly more slowly than CTL responses in macaques. This finding was true whether we compared CTL responses in humans with natural CTL responses in naïve and vaccinated macaques or just in naïve macaques. We found that the average difference between humans and macaques in killing by a single CTL response was ≈0.22 day⁻¹, equivalent to a difference of ≈10⁷ infected cells per day in humans (20). A difference of 0.22 day⁻¹ constitutes a substantial effect and is greater than the most marked interindividual variation seen in humans (4). This result raises the question of “why is the CTL response so much more effective in macaques?” One possibility is that HIV has been, in effect, serially passaged through the human population and is therefore well adapted to evade presentation by common HLA alleles (21–23). SIV and SHIV, on the other hand, do not naturally infect macaques, and challenge strains are likely to have been passaged through a limited number of hosts. They are therefore likely to be less well adapted to the macaque cellular immune response. Another possible explanation is that experimentally infected macaques are often those with the protective alleles Mamu-A∗01 and –B∗17. So it could be the case that macaques used for vaccine studies select for escape more rapidly than average. However, a priori it seems unlikely that the alleles associated with good prognosis are those that rapidly select for escape. Indeed, in humans, protective alleles appear to be associated with decreased frequency of escape (22).

There is no evidence that humans have broader CTL responses (i.e., recognize more epitopes) than macaques (24–29), so, given the observation that individual CTL responses kill virus-infected cells more rapidly in macaques than in humans, we conclude that the total CTL response kills immunodeficiency virus-infected cells more rapidly in macaques than in humans. Our work does not enable us to determine whether CD8⁺ T cells from macaques kill at a faster rate because there are more CD8⁺ T cells targeting each peptide or because the per-CD8⁺ T cell rate of killing is higher. A useful study that would begin to address this question would be to systematically compare the magnitude of the immunodeficiency virus-specific CD8⁺ T cell response between humans and macaques.

Our estimates of CD8⁺ T cell killing are high, indicating that the majority of infected cell death in macaques is attributable to CD8⁺ T cells. This conclusion is consistent with some (but not all) vaccination studies, CTLA-4 blockade, CD8⁺ T cell depletion, and mathematical modeling, which suggest that CD8⁺ T cells play an important role in controlling immunodeficiency virus infection in macaques (30–34).

The total death rate of productively infected cells has been estimated by quantifying the rate of viral decline after primary peak viremia or the start of therapy. These estimates of total infected cell death are similar in SIV and HIV infections (35–39). If rates of CTL-dependent killing are much faster in SIV infections, then it would suggest that rates of CTL-independent killing must be slower. Although there are some in vitro data that are consistent with this hypothesis showing that both activation-induced cell death (40) and viral cytotoxicity (41, 42) may be reduced in SIV infection, it remains a hypothesis to be tested in studies that evaluate the in vivo CTL-independent death rate of productively infected cells in humans and nonhuman primate models.

In macaque models of other human viruses (e.g., poliovirus, measles viruses) the animals are permissive to infection by the human virus. HIV does not reliably infect macaques (43), and it is therefore necessary to use the related viruses SIV and SHIV. Thus, as noted in ref. 44, macaque models of HIV infection are “pathogen models” as well as “animal models.” In this context we found that, independently of differences in the cellular immune response, SIV and SHIV appear to be less tolerant of CTL escape mutations than HIV. Consequently, CTL escape variants may emerge with greater frequency in vaccinated humans than in vaccinated macaques. Given that long-term failure of viral control in vaccinated macaques has already been associated with CTL escape (8) it could present a formidable problem in humans.

The method we have developed is a promising tool for comparing animal models to assess which model best recapitulates CTL control in humans. For instance, differences between the various macaque species (e.g., rhesus and pigtail) or between different pathogenic viral strains (e.g., SIVmac251 and SIVsmE660) could be elucidated. Our work suggests that a sampling interval of no less than 50 days should be used to fully capture CTL escape.

Preclinical vaccine immunogenicity and efficacy are routinely evaluated in SIV- and SHIV-infected nonhuman primates. Clearly these models cannot be expected to mimic every aspect of HIV infection in humans. It is therefore important to understand the similarities and differences and interpret CTL vaccine studies accordingly. Here, we have compared the in vivo CTL response in humans and macaques. Our results show that it may be easier to elicit a protective lytic CTL response in macaques than in humans for two reasons: (i) CTLs kill productively infected cells more quickly in macaques than in humans and (ii) CTL escape variants have higher fitness costs in macaques than in humans.

Methods

Model of Infected Cell Dynamics.

Productively infected cell dynamics were assumed to follow first-order kinetics (see Assumptions of the 2D Model for discussion of this assumption and the effects of relaxing it). Here, we refer to the epitope that is no longer recognized because of mutation as the “escape epitope.” CD4⁺ cells productively infected with WT virus (y) replicate at a net rate a (net of all factors except CTL-mediated death), are killed by CTLs recognizing epitopes other than the escape epitope at a rate b and are killed by CTLs recognizing the escape epitope at a rate c. CD4⁺ cells productively infected with a CTL escape variant (x) replicate at a net rate a′ (net of all factors except CTL-mediated death) and are killed by CTLs recognizing epitopes other than escape epitope at a rate b. Infected cell dynamics are therefore represented by:

In a host able to mount the relevant CTL responses (i.e., with the restricting MHC class I allele), the selective advantage of the escape mutant (i.e., the rate of escape) is the difference in growth rate between the escape variant and the WT k = a′ − b − (a − b − c) = c − a + a′.

Many escape mutations will carry a fitness cost that partially offsets the “benefit” to the virus of evading the CTL response. This fitness cost will be the difference in replication rate between the escape variant and the WT strain ϕ = a − a′. If ϕ is > 0, then the mutation has a deleterious effect on viral replication (in the absence of a CTL response).

Quantification of the Rate of Escape (k).

The rate of escape i.e., the rate of outgrowth of a CTL escape mutant compared with the WT (escape mutant growth rate − WT growth rate) is determined by the balance between the rate of lysis evaded (c) and the fitness cost of the mutation (ϕ = a − a′). The rate of escape (k = c − ϕ) was estimated from longitudinal escape data. If p(t) is the proportion of viral strains sequenced that have an escape mutation in the epitope of interest at time t then solving Eq. 1 we have:

where g = y(0)/x(0) and k = c − ϕ.

This model was fit to longitudinal escape data by using nonlinear least-squares regression, and the rate of escape of the variant strain (k = c − ϕ) and g were estimated.

When there were fewer than two points with neither 100% escape variant nor 100% WT only a lower bound on the rate of escape could be calculated. This was done by transforming p(t) in Eq. 2 above to give ln[1/p(t) − 1]. The model was then fit to this transformed data by using linear regression. Transformed p(t) is singular when p(t) = 0 or 1 so an escape variant frequency of 0/n (where n is the number of clones sequenced) was approximated by 1/(n + 1) and n/n was approximated by n/(n + 1).

Quantification of the Fitness Cost of Escape Mutants (ϕ = a − a′).

If an escape mutant is transmitted to a subject lacking a MHC class I allele that binds the peptide in which the escape mutation has arisen then the selection pressure for the escape mutation will be lost. Both the WT and the variant strain will face the same CTL response, and their relative dynamics will be determined by the difference in their net replication rates (ϕ = a − a′). If the escape mutation carried a fitness cost then the virus will tend to revert to WT. If the mutation was cost-neutral then it will tend to be stable over time. The proportion of viral sequences with an escape mutant will still be described by Eq. 2 but now there is no CTL response against the escape epitope so c = 0. By fitting Eq. 2 with c = 0 to longitudinal reversion data the fitness cost of the escape mutation, ϕ = a − a′, was estimated.

Estimate of the Average Rate of Killing of Productively Infected Cells by a CTL Response Against a Single Epitope (c).

The average rate of escape (k = c − ϕ) of a CTL escape variant was estimated from longitudinal escape data. The average fitness cost (ϕ = a − a′) of a CTL escape variant was estimated from longitudinal reversion data. Putting these two estimates together we quantified c = k + ϕ, the average rate of killing of productively infected cells by a CTL response against a single epitope.

Statistics: Comparing Sampling Time-Dependent Rates of Escape.

Because the escape rates calculated were a function of the time interval between observations it was necessary to compare escape rates in humans and macaques observed with similar frequencies. This process was done by dividing the escape rate estimates into groups with similar average time intervals between the observations, comparing the escape rate estimates between humans and macaques within each group with Wilcoxon–Mann–Whitney's exact test, and then combining the resulting P values by using Fisher's method and testing the resulting statistic by using Fisher's χ² (45). Seven different grouping strategies (ways of dividing the escape rate data into groups with similar average time intervals between the observations) were considered to ensure robustness of results to choice of grouping strategy. Results from one representative grouping strategy are reported (data divided into four equal-sized groups; five human data points with very long time intervals between observations that had no macaque equivalent were discarded). Repeating the tests with ANOVA gave very similar results. ANOVA and Wilcoxon–Mann–Whitney tests were performed by using SPSS version 10.0.5. All P values reported are two-tailed. Sampling intervals for the escape and reversion data sets are detailed in supporting information (SI) Table 3 and SI Table 4.

Quantifying the Impact of CTL Decline.

There is a possibility that as WT virus is replaced by the variant strain the CTL response to the WT will decline because of loss of antigen stimulation. This decline would lead to a reduction in the selection pressure for escape during the escape event and thus an underestimate of the selection pressure before escape. We have previously shown (4) that explicitly allowing for such a decline has little impact on the estimate of escape rates. Furthermore, for this to explain the higher rates of escape in macaques CTLs would need to decline much more rapidly in humans than in macaques. It was possible to estimate the rate of CTL decline during escape in 18 cases of escape (9 in humans and 9 in macaques). This analysis showed no evidence for more rapid decline of CTL in humans compared with macaques (P = 0.97; Wilcoxon–Mann–Whitney one-tailed).

Robustness of Conclusions to Model Changes.

To test the robustness of our conclusions to changes in the model we performed a “model-independent” analysis. We estimated the rate of escape and the rate of reversion by the inverse of the time to escape and the time to revert respectively (see SI Appendix for further details). This analysis fully supported our conclusions that: (i) CTL killing is significantly more rapid in macaques than in humans and (ii) escape variants carry a significantly greater fitness cost in macaques than in humans.

Underlying Viral Replication Kinetics.

A paper by Ganusov et al. (46) showed that underlying viral kinetics can impact escape rate and reversion rate estimates. To ensure that differences in replication kinetics were not distorting the comparison of escape and reversion between humans and macaques we examined the literature to check that estimates of rates of viral replication in acute and chronic infection were similar in humans and macaques. This analysis showed that viral replication kinetics were comparable in humans and macaques (see SI Appendix for details).

Assumptions of the 2D Model.

Our simplified model (Eq. 1) contains two important assumptions compared with the widely used standard model of viral dynamics (39). Specifically, we assume quasiequilibrium between virus-infected cells and free virus on the time scale of escape and reversion and assume that the difference in growth rate between the escape variant and the WT that we quantify is robust to simplifying assumptions affecting both strains (such as neglecting target-cell limited growth). Although these assumptions are routinely made in qualitative work they are not necessarily adequate when we move to a quantitative understanding as we do here. We therefore tested the impact of these assumptions on our estimates of the escape rate. This analysis (see SI Appendix for further details) showed that use of these simplifying assumptions had minimal impact on our estimates.

Abbreviations

CTL

cytotoxic T lymphocyte

SIV

simian immunodeficiency virus

SHIV

simian–human immunodeficiency virus.