HIV control is mediated in part by CD8+ T-cell targeting of specific epitopes

Abstract

We investigated the hypothesis that the correlation between the class I HLA types of an individual and whether that individual spontaneously controls HIV-1 is mediated by the targeting of specific epitopes by CD8(+) T cells. By measuring gamma interferon enzyme-linked immunosorbent spot (ELISPOT) assay responses to a panel of 257 optimally defined epitopes in 341 untreated HIV-infected persons, including persons who spontaneously control viremia, we found that the correlation between HLA types and control is mediated by the targeting of specific epitopes. Moreover, we performed a graphical model-based analysis that suggested that the targeting of specific epitopes is a cause of such control--that is, some epitopes are protective rather than merely associated with control--and identified eight epitopes that are significantly protective. In addition, we use an in silico analysis to identify protein regions where mutations are likely to affect the stability of a protein, and we found that the protective epitopes identified by the ELISPOT analysis correspond almost perfectly to such regions. This in silico analysis thus suggests a possible mechanism for control and could be used to identify protective epitopes that are not often targeted in natural infection but that may be potentially useful in a vaccine. Our analyses thus argue for the inclusion (and exclusion) of specific epitopes in an HIV vaccine.

Importance: Some individuals naturally control HIV replication in the absence of antiretroviral therapy, and this ability to control is strongly correlated with the HLA class I alleles that they express. Here, in a large-scale experimental study, we provide evidence that this correlation is mediated largely by the targeting of specific CD8(+) T-cell epitopes, and we identify eight epitopes that are likely to cause control. In addition, we provide an in silico analysis indicating that control occurs because mutations within these epitopes change the stability of the protein structures. This in silico analysis also identified additional epitopes that are not typically targeted in natural infection but may lead to control when included in a vaccine, provided that other epitopes that would otherwise distract the immune system from targeting them are excluded from the vaccine.

FIG 1

Ability of HLA types and epitope targeting to predict control. Shown are receiver operating characteristic (ROC) curves, plotting the true-positive rate versus false-positive rate of L1-regularized logistic regression models for the prediction of viral control on 341 subjects with chronic HIV infection. Models were trained using a mix of 2-digit and 4-digit resolution (left), as described in the text, and using all 4-digit types (right). The diagonal line represents uninformed predictions. The curve for predictions based on HLA restriction and epitope targeting deviates significantly from the curve for predictions based on HLA restriction alone, indicating significantly improved predictions when epitope targeting data are incorporated. Adding HLA typing information to epitope targeting does not improve predictions. P values are estimated using bootstrap estimates of the standard errors of the areas under the curves (AUC) and test adjacent curves as indicated.

FIG 2

Graphical models used to investigate the direction of cause and effect between targeting and viral control. Nodes correspond to variables, and an arc from one node to another reflects the assumption that the first node is a cause of the second. Thus, model A reflects the assumptions that HLA types are causes of control and that HLA and (lack of) control are causes of epitope targeting. (Not shown in the figure but present in the model are additional arcs from HLA nodes to epitope nodes corresponding to the known influence of HLA on epitope binding.) Model B reflects the assumptions that HLA types are causes of targeting of specific epitopes, which in turn are causes of control.

FIG 3

Structural entropy for protective, nonprotective, and nonsignificant epitopes. The structural entropy of an epitope can be viewed as an in silico estimate for how readily the virus can mutate in that region without harming the virus. Each point corresponds to a specific epitope from a protein (indicated by color). Epitopes are grouped as being protective, nonprotective, or nonsignificant based on the analysis of the ELISPOT data. There is a significant difference between the structural entropies of the protective and nonprotective epitopes (P = 0.001 using a logistic regression test in which the dependent variable is protective versus nonprotective and the independent variables are the structural entropy and the protein domain in which the epitope resides). The circled data point corresponds to the envelope epitope RW9, which spans important protein-protein interaction interfaces that mediate coreceptor, CD4, and gp41 binding. The reported P value includes this point.