Total Assessment of HIV-Specific CTL Responses: Epitope Clustering, Processing Preferences, and the Impact of HIV Sequence Heterogeneity
Nicole Frahm1, Philip J.R. Goulder1,2, and Christian Brander1
1Partners AIDS Research Center, Massachusetts General Hospital, Boston, USA
2The Peter Medawar Building for Pathogen Research, Oxford, UK.
Introduction
The HIV Immunology database at the Los Alamos National Laboratory has collected data on HIV-specific cellular immune responses for over 8 years now and the list of targeted regions within the HIV protein sequences has been growing steadily. These compiled data and our own studies using comprehensive sets of overlapping peptides indicate that almost all parts of the viral protein sequence can be targeted by virus-specific T cells, especially CTL Addo03,Frahm03. HIV is the pathogen that has been characterized most extensively in terms of T-cell epitope distribution and the well-defined epitope landscape of HIV has allowed for a number of studies beyond assessing CTL activity in relation to HIV disease progression Brander02.
Targets of HIV-specific CTL
Whilst in the early years of HIV CTL epitope mapping, attention was focused on structural proteins, more recent studies have included regulatory and accessory proteins as well Tomiyama99b,Altfeld01a,vanBaalen97,Addo00,Addo02. High-throughput assays such as intracellular cytokine staining (ICS), and the Elispot assay are now routinely used to assess genome wide immune responses to HIV Edwards02,Frahm03,Betts01,Addo03,Novitsky01,Novitsky02. This is especially true for the characterization of CD8+ CTL responses, but newer data also include the identification of Th cell activity. Studies from several labs, including ours, using overlapping peptide sets spanning the entire HIV protein sequence have now shown that at least 90% of these peptides can be targeted by HIV-specific CTL, indicating that all viral proteins undergo appropriate antigen processing in vivo and that epitopes from all HIV proteins can be effectively presented to CD8 T cells Addo03,Frahm03. However, there are specific patterns among these responses which will impact HIV vaccine design and which can potentially help to address more fundamental aspects of antigen processing, antigen presentation and T-cell repertoire development Yusim02.
Of special interest for these extended studies, but also for questions of CTL escape and (sub-unit)-vaccine development, is the identification of optimally defined CTL epitopes. Since 1995, largely through the voluntary contributions of unpublished data from many laboratories, regularly updated lists of "optimal CTL epitopes'' have been made accessible through the Los Alamos National Laboratory's HIV database Brander95. This year's update again adds a number of new epitopes whilst some others were removed as they were erroneously included before (mainly some HLA-A*0201 restricted epitopes from our own lab which were based on epitope prediction only and which were not defined with the same stringency as the other epitopes in this list). While the earliest reports clearly focused on alleles common in individuals infected early in the US epidemic, more attention is now given to individuals of non-Caucasian descent Frahm03,Sabbaj03. In addition, epitopes from non-clade B infections are increasingly identified Novitsky02,Novitsky03,Bond01,Fukada02,Lynch98,Sriwanthana01,Goulder01a. The identification of these epitopes provides valuable information for vaccine development in non-Caucasians and non-clade B infection.
In addition, these new epitopes, when characterized in full detail, can provide important insights into HLA binding motifs for these less well characterized alleles; again facilitating the design of a potential HIV vaccine.
Clearly, these databases and prediction softwares can profit from each other and facilitate the future identification of T-cell targets in HIV and other infections.
Immunodominant regions in HIV protein sequences
As mentioned above, the described optimal CTL epitopes are not evenly distributed over the entire viral genome. Rather, there are regions where many epitopes overlap. This phenomenon has been described as early as 1993 and various explanations have been put forward Goulder00c,Buseyne93. Two factors that seem to significantly contribute to this epitope clustering appear to be viral sequence heterogeneity and processing preferences Yusim02.
Sequence heterogeneity affects all HIV proteins, albeit to variable degrees. Relatively conserved regions in Gag and Nef have been identified as immunodominant regions in a study of more than 150 individuals of different ethnicities Frahm03. Independently of the HLA background, these clade B infected individuals made strong responses to the peptides spanning these regions. When comparing the sequence heterogeneity in published clade B sequences, these data also show that peptides with low sequence entropy (more conserved) are targeted more frequently than epitopes with higher entropy. It is likely that these differences are due to the fact that the average phylogenetic distance of the test reagent (consensus B sequence) to an individual`s autologous viral sequence is larger in higher variable regions than in more conserved ones and thus, responses against the less conserved peptides are not detected due to differences between test reagent and inoculum sequence Yusim02,Gaschen02.
In addition to sequence incompatibility between test reagent and autologous virus, certain regions of the HIV protein sequence may not be processed and presented very effectively. Although 86% of our overlapping peptide sets used in the study above were targeted by at least one individual in the cohort of 150 people, there are still some relatively conserved peptides that do not seem to induce a detectable CTL response in natural HIV infection Frahm03. These peptides may lie within stretches of viral proteins that are relatively resistant to proteasomal digestions or may lack adequate "Transporter associated with Antigen Processing'' (TAP) binding motifs Brander02,Yusim02. The HIV Immunology database provides valuable web links to software where sequences of choice can be analyzed for proteasomal processing preferences (NetChop by C. Kesmir et al., http://www.cbs.dtu.dk/services/NetChop/). Recent work by Yusim et al., demonstrates the accuracy and predictive potential of this algorithm and its usefulness in identifying CTL epitopes Yusim02.
Together, these studies indicate that CTL epitope clustering may reflect the biased detection of these responses in rather conserved regions and that processing preferences may play an important role in providing processed antigen. In addition, sequence variability may not only affect CTL recognition but could also have an effect on processing of viral proteins Yellen-Shaw97. Although we have been unable to show such an effect for the flanking regions of the immunodominant, HLA-A*0201 restricted CTL epitope SL9 (SLYNTVATL) in HIV Gag p17, other studies outside the HIV field suggest that escape from processing may be an effective means of immune evasion Yellen-Shaw97,Kuckelkorn02,Gileadi99,Brander99. These studies also highlight the importance of defining T-cell targets in maximal detail, so that prediction algorithm such as NetChop and binding motif algorithms can be optimized by a precisely characterized training set of defined epitopes. In addition, in order to discriminate between processing escape and escape from T-cell receptor recognition or HLA binding, the boundaries of targeted epitopes need to be optimally determined. The present listing is designed to provide these data specifically for HIV derived epitopes and we therefore still separate CTL epitopes in a list of optimally and suboptimally defined epitopes. We hope that this discrimination continues to provide support for the HIV immunologists and laboratories involved in antigen processing and presentation, who want to take advantage of the exceptionally well defined epitope landscape of HIV.
As every year, we would like to express our gratitude to the large number of researchers in the field who continuously contribute to this database. We very much welcome any criticism, comments and additions to this list since we are sure that some epitopes will unintentionally escape our attention, despite close monitoring of the literature. Also, pertinent information, such as resources for single HLA allele expressing cell lines, HLA subtype information and new technologies for CTL epitope mapping could be listed or referenced in this list, providing additional help to problems encountered by investigators.
Acknowledgments
The mostly unpublished data added to this years update stemming from the AIDS Research Center at Massachussetts General Hospital have been largely funded by an NIH contract (#NO1-A1-15442) supporting HLA typing and HIV CTL epitope definition in non-Caucasian populations and non-clade B HIV infection.
Link to the tables of Optimal HIV epitopes.
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