Differential effects of HIV viral load and CD4 count on proliferation of naive and memory CD4 and CD8 T lymphocytes

Abstract

We previously showed that HIV infection leads to expansion of a rapidly proliferating pool (s(1)) of CD4 and CD8 T lymphocytes. In the current study, we used in vivo labeling with bromodeoxyuridine to characterize the kinetics of naive, memory, and activated (HLA-DR(+)/CD38(+)) subpopulations of CD4 and CD8 T lymphocytes, and to examine the relationship between kinetic parameters and baseline CD4 counts, HIV viral load, potential markers of microbial translocation, and cytokine levels. Activated cells showed the highest proliferation rates, followed by effector and central memory cells, with naive cells showing the lowest rates, for both CD4 and CD8 T cells. HIV viral load correlated with s(1) of CD4 and CD8 effector memory cells, as well as CD8 naive cells, whereas CD4 cell counts correlated inversely with naive CD4 s(1). Endotoxin levels showed a weak negative association with CD4 but not CD8 s(1). INF-γ and TNF-α were associated with s(1) for CD4 and CD8 cells, respectively. Thus, HIV is the primary driving force behind the activation and proliferation of most subsets of both CD4 and CD8 T lymphocytes, whereas naive CD4 cell proliferation likely represents a homeostatic response. Microbial translocation does not appear to play an important role in this proliferation.

Figure 1

Kinetics of BrdU-labeled lymphocytes in blood. Graphs represent the kinetics for total CD4 and CD8 T cells (A), and naive, central memory, effector memory, and activated subsets of CD4 (B) and CD8 (C) T cells. The x-axis represents the days following the BrdU infusion; and the y-axis, in log₁₀ scale, the percentage of BrdU-positive cells as detected by flow cytometry. Data represent the geometric mean (± SD) for the first infusion for all 41 patients (panel A; Table 1, dataset 2) and the 22 patients for whom the BrdU kinetics of the subsets were also available (panels B-C; Table 1, dataset 3).

Figure 2

BrdU labeling kinetics of total CD4 and CD8 T cells and their subsets for a representative patient. (Left panels) Data from CD4 cells. (Right panels) Data from CD8 cells. The continuous lines represent the best fit of the experimental data (dots) to the model equations.

Figure 3

Correlation between CD4 s₁ and CD8 s₁. Thirty-one sets of kinetics with VL > 50 copies/mL (A) and 16 sets of kinetics with VL < 50 copies/mL (B). ρ represents the Spearman rank correlation coefficient. A significant correlation is seen for patients with VLs > 50 copies/mL that is lost in patients with VL < 50 copies/mL.

Figure 4

Comparison of size of the rapidly proliferating subpopulation (s₁) of cells in CD4 and CD8 T lymphocyte subsets. Significant differences between groups are indicated by the lines above the groups, together with the P values. Bar (black) represents the median value. One patient from CD4 effector memory and a different patient from CD8 naive subset were excluded from the analysis because of unusually high model estimates, statistically an outlier.