Abstract
HIV infection is controlled immunologically in a small subset of infected individuals without antiretroviral therapy (ART), though the mechanism of control is unclear. CD8+ T cells are a critical component of HIV control in many immunological controllers. NK cells are also believed to have a role in controlling HIV infection, though their role is less well characterized. We used mass cytometry to simultaneously measure the levels of expression of 24 surface markers on peripheral NK cells from HIV-infected subjects with various degrees of HIV natural control; we then used machine learning to identify NK cell subpopulations that differentiate HIV controllers from noncontrollers. Using CITRUS (cluster identification, characterization, and regression), we identified 3 NK cell subpopulations that differentiated subjects with chronic HIV viremia (viremic noncontrollers [VNC]) from individuals with undetectable HIV viremia without ART (elite controllers [EC]). In a parallel approach, we identified 11 NK cell subpopulations that differentiated HIV-infected subject groups using k-means clustering after dimensionality reduction by t-neighbor stochastic neighbor embedding (tSNE) or linear discriminant analysis (LDA). Among these additional 11 subpopulations, the frequencies of 5 correlated with HIV DNA levels; importantly, significance was retained in 2 subpopulations in analyses that included only cohorts without detectable viremia. By comparing the surface marker expression patterns of all identified subpopulations, we revealed that the CD11b+ CD57- CD161+ Siglec-7+ subpopulation of CD56dim CD16+ NK cells are more abundant in EC and HIV-negative controls than in VNC and that the frequency of these cells correlated with HIV DNA levels. We hypothesize that this population may have a role in immunological control of HIV infection.IMPORTANCE HIV infection results in the establishment of a stable reservoir of latently infected cells; ART is usually required to keep viral replication under control and disease progression at bay, though a small subset of HIV-infected subjects can control HIV infection without ART through immunological mechanisms. In this study, we sought to identify subpopulations of NK cells that may be involved in the natural immunological control of HIV infection. We used mass cytometry to measure surface marker expression on peripheral NK cells. Using two distinct semisupervised machine learning approaches, we identified a CD11b+ CD57- CD161+ Siglec-7+ subpopulation of CD56dim CD16+ NK cells that differentiates HIV controllers from noncontrollers. These cells can be sorted out for future functional studies to assess their potential role in the immunological control of HIV infection.