During the chronic phase of HIV-1 infection, polyfunctional CD8؉ T cell responses, which are characterized by a high frequency of cells able to secrete multiple cytokines simultaneously, are associated with lower virus loads and slower disease progression. This relationship may arise for different reasons. Polyfunctional responses may simply be stronger. Alternatively, it could be that the increased functional diversity in polyfunctional responses leads to lower virus loads and slower disease progression. Lastly, polyfunctional responses could contain more CD8 ؉ T cells that mediate a specific key function that is primarily responsible for viral control. Disentangling the influences of overall strength, functional diversity, and specific function on viral control and disease progression is very relevant for the rational design of vaccines and immunotherapy using cellular immune responses. We developed a mathematical model to study how polyfunctional CD8 ؉ T cell responses mediating lytic and nonlytic effector functions affect the CD4 ؉ T cell count and plasma viral load. We based our model on in vitro data on the efficacy of gamma interferon (IFN-␥) and macrophage inflammatory protein 1 (MIP-1)/RANTES against HIV. We find that the strength of the response is a good predictor of disease progression, while functional diversity has only a minor influence. In addition, our model predicts for realistic levels of cytotoxicity that immune responses dominated by nonlytic effector functions most positively influence disease outcome.
IMPORTANCE
It is an open question in HIV research why polyfunctional CD8؉ T cell responses are associated with better viral control, while individual functional correlates of protection have not been identified so far. Identifying the role of CD8 ؉ T cells in HIV-1 infection has important implications for the potential development of effective T cell-based vaccines. Our analysis provides new ways to think about a causative role of CD8 ؉ T cells by studying different hypotheses regarding why polyfunctional CD8 ؉ T cells might be more advantageous. We identify measurements that have to be obtained in order to evaluate the role of CD8 ؉ T cells in HIV-1 infection. In addition, our method shows how individual cell functionality data can be used in population-based virus dynamics models.