On the role of CD8 T cells in the control of persistent infections

Abstract

The control of pathogen density during infections is typically assumed to be the result of a combination of resource limitation (loss of target cells that the pathogen can infect), innate immunity, and specific immunity. The contributions of these factors have been considered in acute infections, which are characterized by having a short duration. What controls the pathogen during persistent infections is less clear, and is complicated by two factors. First, specific immune responses become exhausted if they are subject to chronic stimulation. Exhaustion has been best characterized for CD8 T cell responses, and occurs through a combination of cell death and loss of functionality of surviving cells. Second, new nonexhausted T cells can immigrate from the thymus during the infection, and may play a role in the control of the infection. In this article, we formulate a partial-differential-equation model to describe the interaction between these processes, and use this model to explore how thymic influx and exhaustion might affect the ability of CD8 T cell responses to control persistent infections. We find that although thymic influx can play a critical role in the maintenance of a limited CD8 T cell response during persistent infections, this response is not sufficiently large to play a significant role in controlling the infection. In doing so, our results highlight the importance of resource limitation and innate immunity in the control of persistent infections.

Figure 1

(Color online) Time series simulations of persistent infections using the model defined in Eqs. 1, 2, and 4. (Red) Pathogen P; (black) T cells X(t). The fill under the T cell curve shows the fraction of cells with a given exhaustion level, similar to a stacked bar chart. The distribution for the case with thymic influx (A) converges to the distribution shown in Fig. 2. Without thymic influx (B) the T cell population decays to zero and has very little heterogeneity at any given time. Numerical values of the model parameters used in the simulation can be found in Table 1.

Figure 2

Stationary solution of Eq. 1 when P is held constant. The T cell population in Fig. 1A is converging to the density pictured here. For a > P/(ϕ + P), the density equals zero.

Figure 3

Equilibrium number of T cells given by Eq. 20 as a function of the pathogen density P, when P is held constant. For intermediate levels of pathogen, there is no equilibrium distribution as the T cells do not reach a high enough level of exhaustion and thus can proliferate without limit. High pathogen densities in the model reduce the thymic influx resulting in a decreasing number of T cells at high pathogen density.

Figure 4

Comparison of four limits of the model: no influx and no exhaustion (top left), exhaustion but no influx (top right), influx but no exhaustion (bottom left), and the full model (bottom right). Each subplot shows the nullcline for Eq. 4 (dashed) and the total T cells at equilibrium given by Eq. 20 (solid). Stationary points exist at the intersections of the curves and stable fixed points are annotated (circle). The systems without exhaustion have no persistent state, only a stable fixed point that acute infections orbit around (open circles). The orbits of acute infections drop to values corresponding to pathogen clearance and do not typically converge to these points. The plots illustrate the requirement of exhaustion and constraints on pathogen growth (resource limitations and innate immunity) in the existence of the second stable point (solid circles). We also see the requirement of thymic influx in maintaining a population of T cells during the infection. Without resource limitations or innate immune mechanisms, the dashed curve would be a horizontal line with no persistent state.

Figure 5

(A and B) Comparison of fixed points for different levels of immune affinity and thymic influx, respectively. (A) Reduction in the affinity of the cells of the antigen-specific immune response results in little change in pathogen load but an increase in the numbers of immune cells at the chronic state (solid circles). (B) Three cases for thymic influx: thymic deletion (solid), where thymic influx is reduced by pathogen, normal-influx no-deletion (rounded dash) where thymic influx is unaffected by pathogen, and low-influx no-deletion where thymic influx is reduced by other effects but unaffected by pathogen (dotted). The thymic deletion curve represents the result from the full model (Eqs. 1, 2, and 4). The normal-influx no-deletion has the full influx α (equivalent to setting K ≫ C). In this case, the number of T cells is great enough to clear the pathogen and there is no possibility of a persistent state. The low-influx no-deletion case shows the effect of thymic influx reduced by an effect other than the presence of pathogen such as the reduced thymic capacity associated with old age. In this case, a persistent state is possible.