Human Immunodeficiency Virus Infection Impairs Th1 and Th17 Mycobacterium tuberculosis-Specific T-Cell Responses.

Background: Human immunodeficiency virus (HIV)-infected individuals have a higher risk of developing active tuberculosis (TB) than HIV-uninfected individuals, but the mechanisms underpinning this are unclear. We hypothesized that depletion of specific components of Mycobacterium tuberculosis (Mtb)-specific CD4+ and CD8+ T-cell responses contributed to this increased risk. Methods: Mtb-specific T-cell responses in 147 HIV-infected and 44 HIV-uninfected control subjects in a TB-endemic setting in Bloemfontein, South Africa, were evaluated. Using a whole-blood flow cytometry assay, we measured expression of interferon gamma, tumor necrosis factor alpha, interleukin 2, and interleukin 17 in CD4+ and CD8+ T cells in response to Mtb antigens (PPD, ESAT-6/CFP-10 [EC], and DosR regulon-encoded α-crystallin [Rv2031c]). Results: Fewer HIV-infected individuals had detectable CD4+ and CD8+ T-cell responses to PPD and Rv2031c than HIV-uninfected subjects. Mtb-specific T cells showed distinct patterns of cytokine expression comprising both Th1 (CD4 and CD8) and Th17 (CD4) cytokines, the latter at highest frequency for Rv2031c. Th17 antigen-specific responses to all antigens tested were specifically impaired in HIV-infected individuals. Conclusions: HIV-associated impairment of CD4+ and CD8+Mtb-specific T-cell responses is antigen specific, particularly impacting responses to PPD and Rv2031c. Preferential depletion of Th17 cytokine-expressing CD4+ T cells suggests this T-cell subset may be key to TB susceptibility in HIV-infected individuals.

Mapping the drivers of within-host pathogen evolution using massive data sets.

Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Genetic association studies can potentially identify such interactions. However, extensive and correlated genetic population structure in hosts and pathogens presents a substantial risk of confounding analyses. Moreover, the multiple testing burden of interaction scanning can potentially limit power. We present a Bayesian approach for detecting host influences on pathogen evolution that exploits vast existing data sets of pathogen diversity to improve power and control for stratification. The approach models key processes, including recombination and selection, and identifies regions of the pathogen genome affected by host factors. Our simulations and empirical analysis of drug-induced selection on the HIV-1 genome show that the method recovers known associations and has superior precision-recall characteristics compared to other approaches. We build a high-resolution map of HLA-induced selection in the HIV-1 genome, identifying novel epitope-allele combinations.