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An effective AIDS vaccine must control highly diverse circulating strains of human immunodeficiency virus type 1 (HIV-1). Among HIV-1 gene products, the envelope (Env) protein contains variable as well as conserved regions. In this report, an informatic approach to the design of T-cell vaccines directed to HIV-1 Env M group global sequences was tested. Synthetic Env antigens were designed to express mosaics that maximize the inclusion of common potential T-cell epitope (PTE) 9-mers and minimize the inclusion of rare epitopes likely to elicit strain-specific responses. DNA vaccines were evaluated using intracellular cytokine staining in inbred mice with a standardized panel of highly conserved 15-mer PTE peptides. One-, two-, and three-mosaic sets that increased theoretical epitope coverage were developed. The breadth and magnitude of T-cell immunity stimulated by these vaccines were compared to those for natural strain Envs; additional comparisons were performed on mutant Envs, including gp160 or gp145 with or without V regions and gp41 deletions. Among them, the two- or three-mosaic Env sets elicited the optimal CD4 and CD8 responses. These responses were most evident in CD8 T cells; the three-mosaic set elicited responses to an average of eight peptide pools, compared to two pools for a set of three natural Envs. Synthetic mosaic HIV-1 antigens can therefore induce T-cell responses with expanded breadth and may facilitate the development of effective T-cell-based HIV-1 vaccines.
The development of AIDS vaccines has been advanced recently by demonstrations of increased survival and decreased viral load following vaccination with T-cell vaccines in nonhuman primate models (12, 19, 23, 26, 31, 37). Although such vaccine studies have implied that T cells may contribute to the control of viremia in the highly lethal simian immunodeficiency virus SIVmac251 challenge model, the applicability of these results in human studies remains uncertain. The major concern regarding the efficacy of human immunodeficiency virus (HIV) vaccines in humans is the extraordinary genetic diversity of the virus. The sequence similarity of HIV type 1 (HIV-1) envelope from diverse isolates within a clade can diverge by as much as 15%, and divergence between alternative clades may approach 30% (10). In addition, the diversity of the viral Gag gene product can approach similar levels, particularly in p17 and p15, which are much more diverse than p24 (6), although Gag does not have the extreme localized diversity seen in the highly variable regions of Env (6, 10). While the approach to viral diversity has been addressed in existing vaccines through the use of envelopes derived from representative viruses in the major clades, increasing knowledge about the genetic diversity of naturally occurring isolates has enabled alternative approaches that enhance population coverage of vaccine-elicited T-cell responses.
Approaches under consideration include the use of central gene sequences based on ancestral, consensus, or center-of-the-tree genetic analyses (5, 10, 18, 31, 36). Such prototypes are derived by selection of the most common amino acids at each residue (10, 16, 17, 21, 25, 36), identifying the most recent common ancestor of diverging viruses in a vaccine target population (5, 10, 18, 36), or modeling the sequence at the center of the phylogenetic tree (29), respectively. Peptides based on any of these three centralized protein strategies enhanced the detection of T-cell responses in natural infection relative to the use of peptides based on natural strains; however, all three strategies behaved equivalently (7).
The use of a single M group consensus/ancestral Env sequence has been shown to elicit T-cell responses with greater breadth of cross-reactivity than single natural strains in animal models (31, 36). Such central sequences do not exist in nature, and even phylogenetic ancestral reconstructions are just an approximate model of an ancestral state of the virus (8). Thus, central sequence strategies have provided evidence that various informatically derived gene products can elicit immune responses to T-cell epitopes found in diverse circulating strains, leading to the possibility of using computational strategies to design polyvalent vaccines which optimize T-cell coverage (6, 24). In this study, we have evaluated for the first time the ability of nonnatural mosaic Env immunogens (6) to elicit T-cell responses of increased cross-reactivity against epitopes represented in naturally circulating viruses in animals.
Mosaic HIV-1 envelope genes were derived using an informatic approach, whereby in silico-generated recombinants of natural variants from the Los Alamos database M group Env alignment were created, scored, and selected in combination to optimize the coverage of 9-mers in the global database for a given vaccine cocktail size. While mosaic proteins are artificial constructs that do not occur in nature, they align well to natural proteins, and any short span found in mosaics will tend to be found repeatedly among natural strains (although some of the hypervariable loop regions of Env are so extremely variable that they are not repeated among circulating strains, and this necessitates bridging these regions with segments found in a single strain). In silico recombination breakpoints are constrained to create fusion points found in natural sequences. It is possible to provide increased breadth of coverage with a single mosaic, providing the maximum possible single-antigen diversity coverage for stretches of nine amino acids. Alternatively, multiple mosaics can increase the breadth of representation but have the drawback of requiring the synthesis of additional vectors for clinical use. Mosaics also preserve a natural Env-like sequence to retain normal antigen processing. Here, we have compared single-, double-, or triple-mosaic envelope antigen sets to naturally circulating strains or other derivatives for their ability to elicit immune responses of increased breadth. The data suggest that mosaic HIV-1 envelope sequences provide an approach that may be useful in the development of HIV vaccines that respond to T-cell epitopes represented in naturally circulating strains.
Plasmid DNAs containing five different modifications of the canonical Env gene were generated for each of the antigen sets used in this study: full-length Env proteins (“gp160”) and four variants with short deletions. Deletion variants were as follows: (i) the full-length Env protein with variable loops 1, 2, 4, and 5 deleted (Env gp160ΔVs); (ii) the full-length Env protein with deletion of the fusion domain and the cleavage domains and a shortened interspace between heptad 1 (H1) and heptad 2 (H2) (4) (Env gp160ΔCFI); (iii) the full-length Env protein with deletion of the fusion domain and the cleavage domains, a shortened interspace between heptad 1 (H1) and heptad 2 (H2), and also with variable loops 1, 2, 4, and 5 deleted (Env gp160ΔCFIΔVs); and (iv) the Env protein without the cytoplasmic domain, with deletion of the fusion domain and the cleavage domains, with a shortened interspace between heptad 1 (H1) and heptad 2 (H2), and also with variable loops 1, 2, 4, and 5 deleted (Env gp145ΔCFIΔVs). All modified HIV Env genes were synthesized using human-preferred codons (GeneArt, Regensburg, Germany) (15) or by preparation of oligonucleotides of 75 bp overlapping by 25 bp or of 60 bp overlapping by 20 bp and were assembled with Pwo (Boehringer Mannheim) and Turbo Pfu (Stratagene) as described previously (4, 14). All deletions or other modifications were generated by site-directed mutagenesis using a QuikChange kit (Stratagene, La Jolla, CA). The cDNAs were cloned into a plasmid expression vector, pCMV/R, which mediates high-level expression and immunogenicity in vivo (2, 38).
Mosaic proteins were designed using the methods described by Fischer et al. (6); a web-based suite of tools that enables generation of candidate mosaic sequences for any set of variable pathogen proteins and epitope length sequence coverage comparison of different vaccine antigen candidates is now available (34). Mosaics are optimized as a set for a particular size of cocktail and so were designed separately for the one-, two-, and three-antigen combinations (i.e., the single mosaic is not found in the two- or three-mosaic set). The input data were an unaligned version of the full Env M group alignment from the Los Alamos National Laboratory HIV database, as of July 2006 (restricted to include a single sequence per person). Sequences were generated as recombinants of that set and optimized for 9-mer coverage of that set. Unnatural breakpoints were excluded. We also selected the three natural sequences that in combination provided the optimal 9-mer coverage of that same data set, either with or after exclusion of the V-loops, using the same software suite (9, 34) (http://www.hiv.lanl.gov/content/sequence/MOSAIC). The length of nine amino acids was selected for the optimization criteria because it is the most common length of optimal CD8 epitopes; nearby lengths (8, 10, 11, 12, etc.) also get greatly enhanced coverage through the process of optimizing on 9-mers (data not shown). The full-length Env protein amino acid sequences of the three sets of mosaics, gp160 and various mutants, are shown in Fig. S1 in the supplemental material.
The basic antigen design strategies included the following sets: three natural strains that have been previously studied as a polyvalent vaccine in the modified form gp145ΔCFI, each from different clades (Env ABC); one, two, or three gp160 mosaics (mos.1, mos.2, and mos.3); three natural strains selected to in combination provide optimal M group coverage of gp160 9-mers (nat.3) (CRF01AE, FIN92168 AF219267; clade B, QH0908 AF277072; and clade C, 93IN101 AB023804; listed as clade, sequence name, and accession number) or to provide optimal M group coverage if the V regions were excluded (natΔV.3) (clade C, 99BW46424 AF443084; clade B, QH0908 AF277072; and clade A, KNH1088 AF457063). These baseline sets were further modified to enable direct comparisons of T-cell responses of the full gp160 proteins to previously studied envelope modifications; thus, gp160 responses for a given antigen set were compared to gp145ΔCFI and gp160ΔCFI modifications. A negative control (Control) consisting only of the CMV/R vector was included.
Splenocytes from immunized mice were analyzed by intracellular cytokine staining (ICS) for tumor necrosis factor alpha (TNF-α) and gamma interferon (IFN-γ) T-cell responses against the approximately 100 different peptide pools described below. Responses from CD4+ and CD8+ T lymphocytes were measured separately. The data were analyzed for the magnitude of overall response (strength) and the number of positive responses (breadth). Both TNF-α and IFN-γ responses were measured and analyzed. Interleukin-2 (IL-2) responses were also measured, but there was very little signal and the measurements were dominated by noise, and so these were not included in further analysis. Between 7 and 10 vaccine antigen/protein modification (vac/mod) configurations were tested on 12 separate days. The 12 sets of experiments were grouped into six pairs; in each pair, the same set of antigen configurations was tested. The magnitude of the overall responses varied by a factor of up to about 6 on different days, and this effect was corrected through statistical methods, as described below. Not all configurations of vaccine plus modification were tested, and the number of times a particular configuration was repeat tested ranged from 2 to 12. In Table S1 in the supplemental material, we indicate the number of microtiter plates measured for each vector.
Six- to 8-week-old B6D2F1/J (H2 haplotype b/d) female mice (Jackson Laboratory, Bar Harbor, ME) were used for these immunogenicity studies. Mice (10 per group) were immunized with a total of 15 μg of DNA (100 μl in phosphate-buffered saline), four times at 2-week intervals. Immunizations were administered bilaterally into the muscle of the hind leg using a needle and syringe. The groups included pCMV/R with no insert (Control); vaccines containing 15 μg of one-mosaic plasmid DNA; 7.5 μg of each plasmid in the two-plasmid groups, e.g., a combination of two mosaics; or 5 μg of each plasmid in the three-plasmid groups, e.g., three mosaics, Natural-Strains(Set1), Natural-Strains(Set2), and Trivalent. All animal experiments were reviewed and approved by the Animal Care and Use Committee, Vaccine Research Center (VRC), National Institute of Allergy and Infectious Diseases (http://www.niaid.nih.gov/vrc) and performed in accordance with all relevant federal and National Institutes of Health guidelines and regulations.
In this study, we used 492 Env peptides for ICS stimulation. For this ICS analysis, 15-mer potential T-cell epitope (PTE) peptides (20) were used to evaluate the vaccines as the common standardized panel of HIV-1 peptides for T-cell-based vaccines. The 492 Env peptide sequence set was designed to permit expression of the PTEs found most frequently in the sequences of circulating worldwide HIV-1 strains, based on 549 full-length HIV-1 genome sequences obtained from the Los Alamos National Laboratory HIV sequence database as of February 2005. All synthesized peptides (New England Peptide, Gardner, MA) are 15 amino acids in length with naturally occurring 9-amino-acid sequences that are potential T-cell determinants captured in an unbiased manner (20, 22). Briefly, frequencies were computed for all 9-amino-acid subsequences in the data set, and then 15-mer peptides were selected in order of the summed frequencies of the previously unincluded 9-mers that they contained, using a forward stepwise algorithm. This algorithm selects first for the highly conserved PTEs and then for the less conserved. A total of 492 PTE peptides were generated (for a coverage threshold of 15% ) and grouped into 78 pools of 6 to 12 PTE 15-mer peptides such that the peptides that carried the highest-frequency 9-mers were grouped in the first pool, continuing so that the peptides with the rarest 9-mers were in the 78th pool. All but the pools representing the rarest potential epitopes contained 6 peptides each; the three pools of rarest potential epitopes contained 10 to 12 peptides. We refer to these sets as PTE pools. Four pools with larger numbers of peptides were also tested, with 114 for the first three large pools and 148 for the fourth large pool; we refer to these sets as PTE superpools. Pooled sets of peptides, 15-mers overlapping by 11, corresponding to each of the three envelopes included in the Env ABC polyvalent vaccine were also used as previously described (2-4, 6, 14, 32).
Two weeks after the last immunization, spleens from three mice in each group were harvested aseptically, gently homogenized to form a single-cell suspension, washed, and resuspended to a final concentration of approximately 107 cells/ml. All groups of harvested spleen cells (maximum of 106 cells/peptide pool) were stimulated for 5 h in the presence of 2 μg of anti-CD28 and anti-CD49d monoclonal antibodies/ml (BD PharMingen, San Diego, CA) and also with 10 μg/ml brefeldin A (Sigma, St. Louis, MO). Cells were stimulated for 5 h with (i) 15-mers of 6-peptide pools or 12-peptide pools of PTE, as the target testing stimulating agents; (ii) no stimulus for a background control; (iii) Ebola virus GP protein as the negative control; and (iv) phorbol myristate acetate with ionomycin as the positive control. Env A, Env B, and Env C pools derived from three candidate genes previously described (4) were included as additional controls. Cells were then washed and stained with Vivid dye (Invitrogen, Carlsbad, CA) to determine their viability. FC block monoclonal antibodies were added to the cells, followed by staining with surface antigens (rat anti-mouse cell surface antigens CD3-peridinin chlorophyll protein-Cy5.5, CD4-AlexaFluor700, and CD8-allophycocyanin [APC]-Cy7 [BD PharMingen, San Diego, CA]). The cells were washed again, permeabilized, fixed with Cytofix/Cytoperm, and stained with monoclonal antibodies (rat anti-mouse cell surface antigens CD3-peridinin chlorophyll protein-Cy5.5, CD4-AlexaFluor700, and CD8-APC-Cy7 and rat anti-mouse cytokines IFN-γ-APC, IL-2-phycoerythrin, and TNF-α-phycoerythrin-Cy7 [BD PharMingen, San Diego, CA]), followed by multiparametric flow cytometry analysis to detect the IFN-γ-, IL-2-, or TNF-α-positive cells in the CD4+ or CD8+ T-cell population. Another three mice in each group were subjected to the same analysis 2 days after the initial test to repeat the analysis.
Stained cells were assayed on the BD LSR-II flow cytometer using FACSDiva software (BD Biosciences, San Jose, CA). The data were analyzed with FlowJo 8.6.1 software (Tree Star, Ashland, OR).
The objective of the analysis is to compare the strength and breadth of different vaccine strategies and envelope modifications. A statistical model was used that enabled us to control for the variability between assays done on different days (we will call this the “date effect”) and so to assess the contribution of the vaccine strategy (the “vaccine effect”) to the outcome. These effects should be independent, since the date effect will depend on the measurement process and the variation between mice and the vaccine effect will depend on the vaccine that was given. The usual procedure for dealing with such independent effects is to adopt a balanced experimental design, so that the measurement of a particular vaccine is randomized over the different dates. However, the adoption of such a design was inconsistent with the exploratory manner in which the data were acquired, and in any case, the significance of the date effect was not fully recognized in advance. It turns out that the strengths of the different vaccines vary substantially, by a factor of about 6, but the date effect is roughly comparable in magnitude, complicating the assessment of both strength and breadth.
While measurements with some vaccines were repeated many times, other vaccines were measured on only a few days, often only 2 days. If, for example, the overall response to the vaccine was low, it was not clear whether this was due to the vaccine or to the day on which it was measured. The date effect also complicated the assessment of breadth. If we had used a fixed threshold to assess positivity, as is customary, we would have missed positive responses on days when the overall response was low and would have interpreted random noise as a positive response on days when the overall response was high. We tested this approach and found great variation in the number of positive responses for the same vac/mod on different days. A routine analysis, not correcting for the date effect, would have led to greatly increased noise in the breadth assessments and would have prevented us from making meaningful comparisons.
To deal with this problem, we adopted a statistical model that enabled us to correct for the date effect. We call the corrected data the “date-corrected” data. Using the date-corrected data, we can compare vaccine strengths directly and use a common threshold for assessing positivity. Because the date effect is uncertain, the date-corrected data acquires some additional uncertainty, but the results are nevertheless highly significant. Intuitively, what makes this approach possible is that some of the vaccines were tested on most or all of the dates, and the difference in their responses provides the necessary information about the date effect.
In order to account for the date effect, we modeled the logarithm of the vaccine strength, rather than the strength itself; this converts the multiplicative variation into an additive variation that can then be estimated using a linear model. Accordingly, the following “two-way layout” was adopted:
where lij is the (natural) logarithm of the strength of the responses for the vac/mod i on day j; vi and dj are quantifications of the vaccine and date effects, respectively, in this model; and the ij are identical and independently distributed Gaussian random errors, to account for natural mouse-to-mouse variation and other stochastic effects. We describe how we determined lij below. Note that the log response is additive in vi and dj, which reflects the independence of the date and vaccine effects.
We use the data lij to make estimates, i and j, for the vaccine and date effects. The interpretation of these numbers, roughly speaking, is the following. If vaccines 1 and 2 are measured on the same day, then we expect the response to vaccine 1 to be exp(1)/exp(2) times larger than the response to vaccine 2. Similarly, if the same vaccine is measured on day 1 and day 2, then we expect the response on day 1 to be exp(1)/exp(1) times larger than the response on day 2. The analysis gives only ratios of the strengths (or differences in the log strengths). Thus, we measure all vaccine strengths relative to the negative control and all date effects relative to an arbitrarily chosen fiducial date.
The date-corrected log strength is lij% = lij + 0 − j; this is the log strength that would have been expected had the data been measured on the fiducial date. The expected difference in the date-corrected log strengths, for two different vaccines, depends only on the vaccine, not on the day: E(lij% − li′j%) = i − i′.
The date-corrected responses to individual peptide pools are obtained by multiplying the data on day j by the factor exp(0)/exp(j), where 0 is the fiducial date effect. These are the data that we would expect had the data been measured on the fiducial date. (Note that this is a slight approximation, in that the factor should strictly be the expectation of exp(d0)/exp(dj), but the difference should be small.)
To assess uncertainties in vaccine strength, we calculate the variance of vi − v0, where v0 is the vaccine effect for the negative control. These uncertainties are easily determined from the linear model (equation 1), using standard methods.
The date effects also depended somewhat on the T-cell type and cytokine, so separate models were fitted to all combinations of CD4 and CD8 and of IFN-γ and TNF-α. However, for the same T-cell type, cytokine, and date, different parts of the data gave very similar estimates for dj.
The log strength, lij, was computed in three ways: (i) averaging the logarithm of the largest 10 responses from the PTE pools for the given experiment (the average was restricted to the top 10 responses in order to reduce the effect of noise, which dominates the smaller responses); (ii) averaging the logarithm of responses from the four PTE superpools; and (iii) averaging the logarithm of the responses for the Env A, Env B, and Env C pools. Note that in all cases, lij is the logarithm of the raw measurement, including the noise, which is assumed to scale in the same way as the signal. We thus avoid singularities that would occur if the measurement went to zero. In fact, we estimate the background from the unstimulated counts and replace any smaller measurement by this estimate. The uncertainty in l will increase as the measurements get close to zero, but we do not attempt to model this.
To assess breadth, we counted the number of positive responses to the 78 PTE pools. The data were corrected for the date effect, as described above, using strength estimates based on the PTE pools. A pool was judged to produce a positive response if the date-corrected response exceeded a threshold, which was set separately for each combination of T-cell type and cytokine but was otherwise held constant. When combining data from identical experiments on different days, the combined data were deemed to produce a positive response if the median response exceeded the threshold. The threshold was chosen by examining the PTE pool responses. There were two patterns of response: (i) pools that were clearly positive, in that they consistently showed elevated responses, and (ii) pools that showed either no responses above background or sporadic responses that might have resulted from random noise or rare actual responses. The threshold was chosen to discriminate between these two patterns. A separate threshold was determined for each T-cell type/cytokine combination.
We did measure background counts on each microtiter plate, as well as responses to the negative control. However, these measurements were too noisy to be useful. We instead used the threshold to assess positive response directly, without first subtracting estimated background.
To assess functionality, we computed a matrix whose rows denote particular experiments and whose columns denote small peptide pools. For each element of the matrix, we assigned the number 0, 1, or 2, depending on the number of positive responses for TNF-α and IFN-γ observed for the corresponding experiment and peptide pool. Some experiments were also performed testing IL-2 responses, but the results were weak and sporadic and thus were excluded from further analyses. We then used a standard agglomerative clustering algorithm (35), using Euclidean distances, to cluster the experiments (row vectors) and the peptide pools (column vectors) (http://www.hiv.lanl.gov/content/sequence/HEATMAP/heatmap.html, based on the R package heatmap.2). These cluster patterns are shown on the margins of the heat maps (see Fig. Fig.6),6), which were generated by color coding the responses to indicate those that generated no response (pale yellow), one response to either TNF-α or IFN-γ (orange), or responses to both (red). Statistical support for the various clusters is indicated on the dendrogram branch points, based on the approximately unbiased test of multistep-multiscale bootstraps (33).
The data in these experiments came from a total of 352 microtiter plates, each of which measured IFN-γ or TNF-α responses to CD4+ or CD8+ T cells for a particular vaccine modality (vac/mod) on a particular day. By vac/mod we refer to the DNA vaccine antigen cocktails (including one, two, or three mosaics; three natural strains selected to provide in combination optimal 9-mer coverage; and three natural strains, one each from clades A, B, and C) and the Env modifications (including gp160, gp145ΔCFI, gp160ΔCFI, and gp145ΔCFIΔV and gp145ΔCFIΔV, where ΔV refers to removal of the hypervariable loops and ΔCFI refers to deletions of the cleavage site, fusogenic domain, and spacing of heptad repeats 1 and 2) (4). In some cases, all or part of the data from a given plate were clearly affected by systematic error, as indicated by trends or consistently elevated responses from pools in contiguous regions of the plate. Such plates, of which 17 involved CD8 and two CD4, were left out of the analysis. Thus, a total of 333 plates were used. Among these plates, there was also a very small fraction of small peptide pools (0.3%) for which data were unavailable. We did not try to estimate the missing data.
Polyvalent mosaic vaccines were designed using a genetic algorithm (6) to assemble in silico recombinants of natural Env proteins with breakpoints that do not disrupt the protein and which optimize potential epitope coverage of a diverse population. All stretches of nine amino acids (or 9-mers) were considered potential epitopes, and the presence of rare 9-mers was minimized. The M group sequence alignment from the 2006 HIV database (www.hiv.lanl.gov) was used as a baseline; M designates the “main” group of HIV-1 sequences that includes all of the standard clades (A to K) and their recombinants, the overarching set of diverse HIV sequences responsible for the global HIV pandemic.
We performed separate optimizations for one-, two-, or three-mosaic gp160 DNA vaccine antigen combinations. Each set is comprised of plasmid DNAs encoding distinct proteins, which in combination yield optimal coverage for a given number of antigens. These designs were subsequently modified to parallel Env modifications that have been previously explored as vaccine antigens, including ΔCFI deletions, where ΔCFI refers to deletions of the cleavage site, fusogenic domain, and spacing of heptad repeats 1 and 2 (4), and gp145, to compare the impact of Env modifications relative to intact gp160 on T-cell responses to the various antigens (Fig. (Fig.1A).1A). In addition, three natural strains were selected that in combination optimize coverage of the M group, either including or excluding the hypervariable V1, V2, V4, and V5 regions (ΔVs). The ΔVs were optimized independently and are distinct sets of proteins. We also did several exploratory immunizations using mosaic constructs that had the hypervariable loops removed; no particular benefit was conferred (data not shown), and as these constructs were not as extensively tested as the others, we did not include these results in further analysis. The number of mice that received each vaccine and a description of the peptide pools used in the analysis are shown in Table S2 in the supplemental material.
The rationale for the deletion of the hypervariable regions is that they are often unique and hence would be strain specific, and responses to these regions might divert the vaccine-induced immune response away from more conserved and potentially cross-reactive regions of Env. The code for both designing mosaics and selecting optimal natural sequences is available online at http://www.hiv.lanl.gov/content/sequence/MOSAIC/(34). A comparison of the basic gp160 vaccine designs to the M group sequences from the 2006 HIV-1 database Env alignment (www.hiv.lanl.gov) is shown, and the impact on numbers of potential epitopes lost by deleting parts of the protein in gp145ΔCFI constructs is indicated (Fig. (Fig.1B).1B). We included for comparison and as a positive control a polyvalent vaccine that included one A, one B, and one C clade Env gene (Env ABC). These strains were not optimized for 9-mer coverage and in combination have previously been shown to elicit immune responses (2-4, 6, 14, 32).
To evaluate the T-cell responses to different gp160 mosaics, B6D2F1/J mice were immunized with plasmid DNA vaccines encoding gp160 mosaics encoded by one, two, or three plasmids; one natural strain; or a natural strain with deleted V regions and compared to a mixture of clade A, B, and C gp145s with deletions in the cleavage, furin, and interhelical domains (ΔCFI) described previously (4, 14). Mice (n = 10 per group) were immunized with a total of 15 μg of DNA four times at 2-week intervals. Two weeks after the last immunization, the splenocytes from different groups of three immunized mice were isolated, pooled, and stimulated by different stimulants as in all experiments in this study: unstimulated and 492 Env peptides PTE in 78 pools (p1 to p78). Additional controls included Env ABC peptide pools, an irrelevant peptide pool control, and nonspecific stimulants (data not shown). The CD4 and CD8 cell responses were measured using ICS for IFN-γ and TNF-α (Fig. (Fig.2).2). The minimal threshold response was defined as 2 times that of the negative control. The CD8 response to the 1 plasmid (mos.1) gp160 mosaic was lower than the response to the mos.2 or mos.3 mosaics (Fig. (Fig.2,2, right panel 2 versus 3,4), with four compared to seven positive pools above background. Some peptide pools were shared in common among the three different mosaic combinations, while others were unique.
The responses elicited by the natural-strain plasmids, with or without V region deletions, or gp145ΔCFI trivalent plasmids were decreased relative to the mosaics using the PTE peptide pools. For example, the three-mosaic set induced a CD8 response above 0.25% in seven peptide pools, compared to only two pools with three natural Envs (Fig. (Fig.2,2, right panel 4 versus 5), and these responses were higher in magnitude. Similar results were seen for CD4 responses (Fig. (Fig.2,2, left panel), though the wild-type natural-strain plasmid induced a larger number of CD4 epitope responses. The additional animals in each group were sacrificed and analyzed in the same way, and statistical analysis was applied to the responses measured from the entire group (see below). From the initial observation, it was clear that the gp160 mosaic plasmids elicited detectable T-cell responses against PTE peptide pools after immunization.
We next tested alternative mosaic plasmids, with or without the variable regions, ΔCFI, or both deletions. These vectors were in turn compared to the multiclade Env gp145ΔCFI vectors shown previously to improve the breadth of the Env T-cell response compared to single-strain Env immunogens (14, 32). These comparisons were done as described above (see Fig. S2 to S5 in the supplemental material). The resultant immune responses for the complete groups of six animals were then analyzed to determine their comparative magnitude and breadth of response. Breadth was defined here as the number of responses to different PTE peptide pools. Comparing the number of responses to PTE peptides, it was evident that the responses in these groups immunized with deleted mosaics was lower than those with the wild-type gp160 mosaic, particularly for CD8 responses, although this result may in part reflect mismatches between the immunogen and assay peptides. Nonetheless, we chose to focus on the more immunogenic inserts, two- and three-set gp160 mosaics, according to this criterion.
The analysis of immune responses of diverse immunogen combinations to many peptides was limited technically by the number of animals that could be analyzed on the same day. In order to compare groups analyzed with different immunogens on different days, internal references were used, and we evaluated these data further with a second approach using formal statistical analysis to compare relative vaccine potency. Specifically, we looked at the breadth (number of epitopes) and magnitude of cellular immune responses and attempted to control for interassay variability among samples analyzed on different days. To characterize the ability of different immunogens to elicit a vaccine response, we determined the vaccine effect, the magnitude and breadth of CD4 and CD8 responses, by comparing the IFN-γ and TNF-α ICS responses to the different peptide pools: Env ABC peptide pools, the PTE pools, and the PTE superpools (Fig. 3A and B). Six immunogens were compared; sets of mos.1, mos.2, or mos.3 mosaics and the pool of three natural strains, with or without V regions, in addition to a negative control and a positive control Env ABC vaccine. Reassuringly, the Env ABC vaccine elicited strong responses to the Env ABC peptide pools. Thus, the overall vaccine response tested using autologous peptides suggested a robust response to the matched vaccine. The response of CD4 cells to the vaccine was similar in both the small pools and the superpools, and it was highly significant for both IFN-γ and TNF-α responses (P < 10−4). For CD8 responses to the Env ABC immunogens, the IFN-γ and TNF-α ICS responses were highest against B clade pooled peptides. Among the individual A, B, and C pools, the responsiveness of CD8 T cells was reflected in a rank order, B > A ~ C (by paired Wilcoxon test, B > A, P = 0.0005; B > C, P = 0.003). CD4 T-cell responses were more consistent across all three clade pools, with B ~ A > C, (by paired Wilcoxon test, B > C, P = 0.009; A > C, P = 0.052) (data not shown).
The strength of responses to different vaccines was also assessed by measuring the magnitude of T-cell responses to subgroups of the PTE peptide pools, each containing six peptides. Because peptide pools were used, a strong response might reflect either an intense response to a single peptide in the set or a sum of moderate or low responses to several distinct peptides in the set. For CD8 cells, the strength of the observed responses to the mosaics was much greater than that of the natural proteins or the positive control (Fig. (Fig.3).3). Among the mosaics, the two- and three-mosaic set responses were stronger than the those of single mosaic. The overall strength of the response to PTE pools was much greater than the response to the Env A, B, and C pools. The gp145ΔCFI immunogens induced a consistently weak response, which may have resulted from a loss of epitopes due to the deletions in the open reading frame (Fig. (Fig.1B)1B) which then caused a mismatch between the immunogens with regard to the number of peptides made on the basis of the wild-type sequence. The response profile for CD4 is quite different from that of CD8 (Fig. (Fig.3).3). Here, the strengths of the responses to mosaics and the natural proteins were roughly comparable, and there was less variation in the response of the different vaccines than in CD8 cells. The data suggest that the ΔCFI modification may reduce the response, possibly because of less matching to the ICS assay peptides, but the 145ΔCFI modification, which suppressed the CD8 response, does not do the same for the CD4 response. Finally, vaccine-induced immunity was determined by ICS with PTE superpools. Response strength to the different vaccines was also assessed by measuring T-cell responses to the four peptide pools including large numbers of peptides (Fig. (Fig.3).3). The strength estimates for the large peptide pools are very similar to those obtained with the small peptide pools, despite the fact that they were obtained from separate experiments.
Vaccine breadth was assessed using date-corrected (normalized) responses to the 78 small peptide PTE pools. Data for identical vaccines or vaccine variants on particular dates were pooled, and responses were deemed positive if the median response exceeded a fixed threshold, as described in “Statistical methods” above. The ICS IFN-γ responses in CD8 cells were compared among six different vaccines.
The breadth of the mosaic responses was dramatically greater than that of the natural responses, including both Env ABC and the three natural proteins selected to optimize epitope coverage (Fig. (Fig.4A).4A). The mosaics all show clear spikes for numerous peptide pools. The number of such pools increases from 4 to 10 in the monovalent versus the trivalent mosaics. The best natural set of Envs shows only two weak spikes, at pools 7 and 13. The results for the TNF-α ICS responses in CD8 cells were very similar, both in the magnitude of the response and in the specific peptide pools that test positive (Fig. (Fig.4B4B).
Analogous plots for CD4 IFN-γ and TNF-α ICS revealed similar patterns of responsiveness (Fig. 5A and B). The mosaics still generated more positive responses than the natural proteins, although the difference was less striking. For example, the two-mosaic and three-mosaic sets generated a vigorous IFN-γ response to 15 and 14 pools, respectively, but three natural proteins still did quite well [10 pools each for Env ABC, Natural ΔV, and Natural (N1/N2/N3)].
Although the correction for interassay variation improved the experimental consistency between experiments, the identification of positive responses was not sensitive to uncertainty in the strength correction. In fact, the greater breadth of CD8 response in the mosaics was clear even if no strength correction was used at all. With regard to the threshold, changes in the threshold do affect the number of positive responses, and these changes can affect different vaccines differently, depending on how many positive responses are near the threshold. The CD8 breadth comparisons are very robust to changes in the threshold; the CD4 comparisons are not as robust. Inasmuch as the breadth estimate depends on a choice of threshold and there is uncertainty as to the correct value, the evidence for increased CD4 breadth in the mosaics should be viewed as suggestive but not conclusive.
To evaluate the qualitative nature of the T-cell response, we examined whether these cells synthesized either IFN-γ or TNF-α alone, or both, in response to vaccination, the latter being a surrogate for a multifunctional cytokine production. For this analysis, the responses were evaluated using a heat map representation of functional CD8 responses to IFN-γ and TNF-α, with a threshold of 0.1 (Fig. (Fig.6A).6A). No response, a response to either IFN-γ or TNF-α, or a response to both IFN-γ and TNF-α were represented by increasing “heat” (pale yellow, orange, and red represent negative, monofunctional, and bifunctional responses, respectively). Vaccines that had similar behavior in terms of responses to particular peptide pools are clustered together by row, and peptides pools that had similar patterns of eliciting responses are clustered together in columns. All gp160 two- and three-set mosaics are contained in a single large cluster; i.e., they displayed consistent patterns of frequent bifunctional responses to particular peptide pools. The gp160 mos.1 mosaic responses occupied a single cluster with intermediate reactivity. A cluster for the natural gp160ΔCFI set was also evident. All of the mosaics elicited many bifunctional responses; the pattern is less apparent in the natural proteins, although this may be due to a generally lower overall response. The consistency of the responses to closely related vaccines is apparent. Analysis of the CD4 responses with this heat map revealed that the mosaics, as well as the natural proteins, are broadly bifunctional (Fig. (Fig.6B).6B). Indeed, the number of bifunctional responses exceeds the number of monofunctional responses. The CD4 responses of the mosaics and the natural proteins were quite similar, not only in magnitude and breadth but in the specific peptide pools generating a positive response.
In this report, we have analyzed the ability of HIV-1 mosaic genes encoded in single, double, or triple plasmids to confer increased reactivity against T-cell epitopes derived from commonly circulating viruses. For this analysis, we immunized mice with these plasmids or with previously defined mutants derived from natural isolates and compared their abilities to respond to T-cell epitopes that have been identified in a high proportion of naturally circulating strains. The peptide designs, either those in the mosaics or those in the PTE peptides for immune assays, were based on 9-mers in Env sequences and were independent of major histocompatibility complex (MHC), either human or mouse. Initial analyses could therefore be performed in inbred mice where immune function can be assessed with greater consistency, in a constant genetic background of restricted MHC haplotypes. These data may underestimate the ability of such vectors to elicit responses to a wide array of genes, given the increased MHC complexity in humans and nonhuman primates. At the same time, the possibility remains that Env epitopes have undergone negative selection to escape human MHC recognition, which would act oppositely and possibly reduce immunogenicity in humans.
The data indicate that the mosaic vaccine antigens are able to expand the breadth of T-cell responses to Env PTE. The responses appear to be greatest in animals receiving combinations of two- or three-mosaic inserts that give greater predicted coverage of T-cell epitopes based on informatic analyses. Interestingly, little difference was noted between two- and three-mosaic Env immunizations, possibly because of the restricted MHC haplotype in mice. Whether this would hold true in nonhuman primates or humans will require additional testing.
The reason for the improved immunogenicity of the Env mosaics compared to the optimal natural Env glycoproteins in terms of breadth and magnitude of responses may relate in part to the peptides used for the ICS analysis. The number of responses to three-mosaic proteins was surprisingly enriched over the three natural strains selected to give optimal M group coverage. The best natural proteins and the mosaics were selected relative to the M group collection of HIV sequences in the HIV database. Comparing the gp160 trivalent antigen designs, the set of M group Envs from the database had 44% of its 9-mers perfectly matched in the three-mosaic set, while 34% were matched by the three optimal natural strains (Fig. (Fig.1B).1B). This relatively modest advantage, however, seems unlikely to account for the profound experimental advantage seen for mosaics over natural proteins (Fig. (Fig.55 and and6).6). One possibility is that PTE peptide pools were selected in particular to cover 9-mers that were most common, as were the mosaics, and the relative advantage of the mosaics to the optimal natural proteins is enhanced when tracking 9-mer identities found among the PTE set: three mosaics matched 67% of PTE 9-mers, and the best natural proteins matched only 44%. The number of identical 9-mers (potential CD8+ T-cell epitopes) shared between a vaccine (including all antigens in a polyvalent vaccination) and a given peptide pool can provide a rough estimate of the potential number of CD8+ T-cell responses to that vaccine that could be detected by ICS using that peptide pool. The two- and three-mosaic antigens match more 9-mers than the natural-sequence polyvalent antigens (see Fig. S6 in the supplemental material). In particular, even the two-mosaic vaccine has a higher number of 9-mer matches (match counts) with PTE peptides than any of the natural-sequence candidates, although the latter have three sequences each. The three-mosaic vaccine has higher match counts than the natural-sequence vaccines in 70 of 78 individual pools (see Fig. S6 in the supplemental material) and a large overall advantage (see Table S3 in the supplemental material).
A second factor is that, by design, mosaics minimize the inclusion of rare and unique 9-mers. In contrast, natural strains inevitably contain many unusual, type-specific 9-mers. T-cell reactions against rare variant epitopes found in natural strains would go undetected in PTE-based assays and in fact would be of little use in a vaccinated population. If immunodominant, they could limit the potential to stimulate responses with more useful epitopes with the potential for greater cross-reactivity. For mosaic vaccine antigen designs based on more conserved proteins like Gag (6), we were able to require that every single 9-mer included in a given mosaic antigen set was found at least three times in the population. For the Env-based mosaics used here, however, to span hypervariable regions and weave together intact proteins, we were forced to require inclusion of a small number of rare 9-mers, and three-mosaic gp160 proteins contained 128 9-mers that were found <3 times in the M group alignment used in Fig. Fig.1B.1B. In contrast, the mixture of three natural strains contained 406 9-mers found <3 times in the population. Similarly, the PTE peptides are designed to emphasize the inclusion of the most common potential epitopes and so have more peptides in common with the mosaic proteins. If any of the vaccines elicited responses specific for epitopes that are not included in the PTE sets, they would be missed. The natural three-gp160 cocktail contains 1,072 9-mers that are not found among any of the PTE peptides, while the mos.3 mosaic vaccine has many fewer, 684, so it is possible that the natural vaccine has elicited a higher frequency of responses that were missed by the assay system, because they are specific for the vaccine strain.
While the approach to diversification of envelope immunogens appears to be promising, it is clear that this approach will need to be combined ultimately with an informatic approach to optimize the response to other viral gene products, including Gag and possibly Nef. Whether the addition of a relatively conserved gene product such as polymerase can improve this response further will also require testing. It may be argued that the polymerase is a less effective immunogen for protection against HIV-1 or SIV challenge because this gene product is conserved, is present in low abundance, and is not likely to represent a viral gene product that is highly selected by immune pressure. At the same time, the variability of the envelope gene and the identification of T-cell escape mutants in nonhuman primates and humans suggest that this gene product, as well as the Gag protein, is recognized and under strong selective pressure by the immune system. The viral Env is under further selection by antibodies, which appear to evolve during the course of infection and drive the generation of new mutants (13, 27, 28, 30).
The data here suggest that the informatic approach to enhancement of viral diversity coverage increases the immune recognition of diverse viral epitopes. Whether this increased recognition can give rise to improved protection remains unknown. There are two ways that a polyvalent mosaic might improve a vaccine: the first is in cross-reactive protection against circulating strains, and the second is the potential to block fit immune escape routes. The present study suggests that the mosaic concept should be tested further in nonhuman primate models. In particular, the SIV sooty mangabey virus pool may provide an opportunity to evaluate the efficacy of mosaic immunization in protecting against infectious challenge. A variety of viruses from diverse clades have been identified and in many cases adapted to growth in Indian rhesus macaques (1, 11). By assembling additional viral sequences and generating SIVsmm mosaic sequences and relevant challenge strains, it should be possible to test whether this approach can show efficacy in the context of viral infection and merit further evaluation in human efficacy studies.
The recent results of the STEP trial have suggested that immunization with recombinant adenovirus 5 vectors shows no efficacy in humans and may have the potential to increase HIV acquisition in adenovirus 5-seropositive individuals. While the basis for this lack of efficacy is unknown, it is clear that the induction of a T-cell immune response remains a desirable goal for HIV vaccines. Even in the event that broadly neutralizing antibody immunogens could be elicited, the likelihood that a number of circulating viruses would remain resistant to neutralization or the evolution of variants that would escape antibody neutralization would suggest that stimulatory T-cell responses could help to control infection and/or contain viremia. It is therefore important to recognize that the approach to the development of a T-cell-based vaccine that would address diverse relevant strains of circulating virus remains distinct from the approach to the elicitation of neutralizing antibodies. It is unlikely that the informatic approach derived here to address T-cell diversity would resolve the issues related to broadly neutralizing antibody immunogens, but a combination of this approach and rationally designed broadly neutralizing antibody immunogens may improve the likelihood of containment or prevention of natural HIV infection.
We thank Ati Tislerics for assistance with manuscript preparation, Brenda Hartman for figure preparation, and members of the Nabel lab for helpful discussions and advice.
This work was supported in part by the Intramural Research Program of the National Institutes of Health, Vaccine Research Center, National Institute of Allergy and Infectious Disease, and by Los Alamos National Laboratory directed research funding.
Published ahead of print on 24 December 2008.
†Supplemental material for this article may be found at http://jvi.asm.org/.