Atlas of the HIV-1 Reservoir in Peripheral CD4 T Cells of Individuals on Successful Antiretroviral Therapy

Abstract

Knowing the mechanisms that govern the persistence of infected CD4⁺ subpopulations could help us to design new therapies to cure HIV-1 infection. We evaluated the simultaneous distribution of the HIV-1 reservoir in 13 CD4⁺ subpopulations from 14 HIV-1-infected individuals on antiretroviral therapy to analyze its relationship with HIV-1 transcription, immune activation, and cell proliferation. A unique large blood donation was used to isolate CD4, CD4 resting (CD4r), CD4 activated (CD4a), T naive (T_N), T stem cell memory (T_SCM), T central memory (T_CM), T transitional memory (T_TM), T effector memory (T_EM), circulating T follicular helper (_cT_FH), T_CD20, T_CD32, and resting memory T_CD2^high (_rmT_CD2^high) cells. HIV-1 DNA measured by droplet digital PCR ranged from 3,636 copies/10⁶ in T_TM to 244 in peripheral blood mononuclear cells (PBMCs), with no subpopulation standing out for provirus enrichment. Importantly, all the subpopulations harbored intact provirus by intact provirus DNA assay (IPDA). T_CD32, _cT_FH, and T_TM had the highest levels of HIV-1 transcription measured by fluorescent in situ hybridization with flow cytometry (FISH/flow), but without reaching statistical differences. The subpopulations more enriched in provirus had a memory phenotype, were less activated (measured by CD38⁺/HLA-DR⁺), and expressed more programmed cell death 1 (PD-1). Conversely, subpopulations transcribing more HIV-1 RNA were not necessarily enriched in provirus and were more activated (measured by CD38⁺/HLA-DR⁺) and more proliferative (measured by Ki-67). In conclusion, the HIV reservoir is composed of a mosaic of subpopulations contributing to the HIV-1 persistence through different mechanisms such as susceptibility to infection, provirus intactness, or transcriptional status. The narrow range of reservoir differences between the different blood cell subsets tested suggests limited efficacy in targeting only specific cell subpopulations during HIV-1 cure strategies. IMPORTANCE The main barrier for HIV-1 cure is the presence of latently infected CD4⁺ T cells. Although various cell subpopulations have been identified as major HIV-1 reservoir cells, the relative contribution of infected CD4 subpopulations in the HIV-1 reservoir remains largely unknown. Here, we evaluated the simultaneous distribution of the HIV-1 reservoir in 13 CD4⁺ T-cell subpopulations in peripheral blood from HIV-1-infected individuals under suppressive antiretroviral therapy. We found that the HIV-1 reservoir is composed of a mosaic of cell subpopulations, with heterogeneous proviral DNA, HIV-1 transcription, and activation status. Hence, each cell subpopulation contributes to the HIV-1 persistence through different mechanisms such as susceptibility to infection, rates of intact provirus, transcriptional status or half-life. This research provides new insights into the composition of the HIV-1 reservoir, suggesting that, to be effective, eradication strategies must simultaneously target multiple cell subpopulations.

Keywords: HIV-1; HIV-1 DNA; HIV-1 RNA; HIV-1 cure; HIV-1 reservoir; HIV-1 reservoir size.

FIG 1

Study design flow-chart.

FIG 2

Measurement of HIV-1 DNA and HIV-1 RNA in CD4⁺ T-cell subpopulations. (A) Total HIV-1 DNA measured in 13 sorted CD4⁺ T-cell subpopulations. According to the gating strategy (Fig. S1), some of these subpopulations overlap. (B) Number of HIV-1-infected cells for each CD4⁺ T-cell subpopulation based on the frequency of each CD4⁺ T-cell subpopulation and its levels of total HIV-1 DNA in peripheral blood. (C) Proportion of cells expressing HIV-1 RNA in each CD4⁺ T-cell subpopulation normalized to the medium control. The gray box indicates the cell subpopulations with median HIV-1⁺ RNA levels not different from HIV-1^– individuals. (A to C) For all the panels, each colored symbol represents a single individual. On the right of each panel, matrices depict P values of the differences between paired CD4⁺ T-cell subpopulations. Differences were tested for statistical significance using the Wilcoxon rank sum test followed by the FDR test for multiple comparisons. Box and whisker plots indicate the median, interquartile range, and minimum and maximum values.

FIG 3

Measurement of intact provirus in CD4⁺ T-cell subpopulations. (A) Intact proviral frequencies per 10⁶ cells determined by IPDA for each CD4⁺ T-cell subpopulation from 6 HIV-1-infected individuals. (B) Percentage of intact provirus with respect to total IPDA levels. Total IPDA proviruses were determined as the sum of intact, 5′-defective, and 3′-defective proviruses from each individual. Each symbol represents 1 individual. Open symbols represent values under the limit of detection. Box and whisker plots indicate the median, interquartile range, and minimum and maximum values.

FIG 4

Relationship between HIV-1 DNA, HIV-1 RNA, and parameters of activation, exhaustion, and proliferation for each CD4⁺ T-cell subpopulation. (A) The Nightingale rose plots show the patterns of 7 parameters (total HIV-1 DNA, HIV-1 RNA⁺, Ki-67⁺, PD1⁺, CD38⁺HLA-DR⁺, CD69⁺, HLA-DR⁺) for 10 CD4⁺ T-cell subpopulations. Each wedge represents a parameter studied and is depicted in a different color. The size of the wedge for each parameter depicts a proportional magnitude of the parameter relative to the maximum magnitude across the different CD4⁺ T-cell subpopulations. (B) Three-dimensional graph showing the correlation between total HIV-1 DNA and the number of HIV-1-infected cells in peripheral blood; the bubble size represents the levels of intracellular HIV-1 RNA in each cell subpopulation. T_CD4r and T_CD4a bubbles are not sized according to their intracellular HIV-1 RNA. T_N, T_SCM, and _rmT_CD2^high are given a small bubble size since their intracellular HIV-1 RNA levels are not different from those of HIV-1^– individuals.