Rational Peptide Selection To Detect Human Immunodeficiency Virus Type 1-Specific T-Cell Responses under Resource-Limited Conditions

Identification of epitopes.

We based our analysis on the usage of peptides of 15 amino acids in length. To determine the number of epitopes each peptide contained, we mapped major histocompatibility complex class I (MHC-I)-restricted epitopes, obtained from the Los Alamos database (http://hiv-web.lanl.gov/content/immunology/tables/ctl_summary.html), onto the HIV-1 HXB2 proteome. The HLA restrictions were standardized to the two-digit molecular HLA type nomenclature (8). The epitopes were mapped onto the HXB2 proteome as shown in the examples from HIV-1 Gag p17 (Fig. 1A).

• Open in new tab
• Download powerpoint

FIG. 1.

Generation of peptide scores. (A) Epitopes were mapped onto the HXB2 genome. Epitopes are shown as white boxes under their specific amino acid sequences; their HLA restriction is shown within. Overlapping epitopes were combined to make one continuous epitope region. A hypothetical example is shown in blue (part i); the joined epitope constitutes the HLA-A2-restricted SLYNTVTAL epitope. (B) Generation of Σ_HLA. Each amino acid received a score equal to that of the HLA prevalences for each epitope that covers it. In parts i and ii, examples are given showing the HLA prevalences mapped onto their respective epitopes. For example, the first residue (glutamic acid [E]) is covered by a single HLA-A1-restricted epitope. HLA-A1 has an 8% prevalence within the North American population; therefore, for this amino acid, its Σ_HLA is 0.08. (iii) The Σ_HLA for each amino acid across the whole of p17 is displayed as a heat map above a skyline graph. (C) Entropy is shown for the example sequence (parts i and ii) and mapped across p17 as a heat map above and skyline graph below (part iii). (D) The final peptide score is generated from the division of the sum of the Σ_HLA from each amino acid within the peptide by the sum of the entropy from each amino acid within the peptide. In this example, from peptide 7890, the total Σ_HLA across the peptide equaled 7.00 and the total entropy was 5.98, giving a final peptide score of 1.17.

Amino acid score.

To maximize the recognition of the selected peptides, epitopes were weighted based on the HLA prevalence (as a percentage) of our North American study group. The HLA prevalence for North America was obtained from the dbMHC IHWG Anthropology database (http://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init). Each amino acid within the HIV-1 proteome received a score that equaled the total of the HLA prevalence for each epitope that covered it (Σ_HLA); an example from two 15-mer peptides from p17 is shown (Fig. 1B). When extended across all of HIV-1 Gag p17, the Σ_HLA can be displayed as a heat map (Fig. 1B, part iii). The sum of each amino acid's Σ_HLA contributed to the final peptide score (Fig. 1D).

Entropy.

The variability of HIV-1 antigens is known to interfere with the accurate detection and measurement of HIV-1-specific T-cell responses (1). Differences in sequence between the consensus-based synthetic peptide antigens and an individual's autologous virus often result in false-negative responses. In order to minimize this bias and systematically focus on conserved regions of HIV-1, we computed the amino acid entropy (variability) of sites across the HIV-1 proteome and incorporated this information into our peptide scoring scheme.

In this example, the entropy of each amino acid was determined with full-length subtype B protein sequence alignments of all HIV-1 proteins, downloaded from the Los Alamos database (http://hiv.lanl.gov/content/hiv-db/ALIGN_CURRENT/ALIGN-INDEX.html). Alignments were restricted to one sequence per individual to minimize the sampling bias. Sequences were then realigned by using ClustalW with default gap parameters and the “IUB” DNA weight matrix (10). Manual aligning of variable regions was performed with the Se-Al sequence alignment editor, version 2.0 (http://evolve.zoo.ox.ac.uk/). The HyPhy software package was implemented to compute site-specific Shannon entropy scores for each coding region from the frequency, f, of amino acid A at position i, according to the formula −ΣA f(A_i) ln[f(A_i)] (6, 9). Sites within each protein were aligned with the HIV-1 HXB2 sequence (4) (Fig. 1C, parts i and ii). The sum of each amino acid's entropy within a peptide was used in the final peptide score (Fig. 1D).

Final peptide score.

In this example, the HIV-1 group M consensus (15-mer) peptide sequences, from the NIH ARRRP, were mapped onto the HIV-1 proteome. The final peptide scores were based on the sum of the amino acid's Σ_HLA from across the peptide divided by the sum of the entropy from each amino acid within the peptide (Fig. 1D). Peptides from each HIV-1 protein were then ranked; Table 1 shows an example for HIV-1 Gag p17. To determine the breadth of responses, top-ranking peptides from each protein were selected for use in an ELISPOT assay.

Proof of principle.

To test this method, we screened gamma interferon (IFN-γ) responses from 10 HIV-1-infected subjects. The HLA types of the individuals were unknown. The North American HLA prevalence was applied to generate two panels of peptides (Table 2). Panel A contained peptides that had the highest peptide scores, and panel B contained peptides with the lowest (but still greater than 0) peptide scores. Based on our hypothesis, we predicted that panel A would show the highest number of responses.

The IFN-γ ELISPOT assay was conducted by the standard protocol, as previously described (5, 7). In this example, approximately 4 million PBMC were used to cover each peptide panel and controls. As predicted, the highest number of responses was directed toward panel A (Fig. 2). An average of 7.4 peptides induced responses in panel A, compared to 0 in panel B (P = 0.0053) (Fig. 2).

• Open in new tab
• Download powerpoint

FIG. 2.

Proof of principle. Two panels of 21 peptides were generated by the method described in the legend to Fig. 1. Panel A was selected from the 21 highest-scoring peptides, while panel B was selected from the 21 lowest-scoring (but still scoring >0) peptides. IFN-γ responses from nine (panel A) and five (panel B) HIV-1-infected subjects revealed that panel A was significantly better recognized than panel B (P = 0.0053).

In summary, we have described a novel method for the rational selection of peptides for screening HIV-1-specific CTL responses in resource-limited settings. This protocol targets stable regions of the HIV-1 genome that are rich in epitopes that are specifically recognized by the study population where underlying HLA prevalences are known. In the example given, only 4 million PBMC, which can be isolated from as little as 5 ml of blood, were used and only 26 peptides were needed. If these peptides had been pooled, cell numbers could have been reduced further, although this would reduce the information on the breadth of responses.

This method could easily be adapted to other viruses, such as hepatitis C virus and human cytomegalovirus, and/or expanded to incorporate CD4⁺ T-cell epitopes. Due to the low number of resources needed, this approach will be of particular benefit to in-field therapeutic and vaccine studies, as well as studies conducted under resource-limited conditions.