Infection dynamics in HIV-specific CD4 T cells: does a CD4 T cell boost benefit the host or the virus?

Abstract

Recent experimental data have shown that HIV-specific CD4 T cells provide a very important target for HIV replication. We use mathematical models to explore the effect of specific CD4 T cell infection on the dynamics of virus spread and immune responses. Infected CD4 T cells can provide antigen for their own stimulation. We show that such autocatalytic cell division can significantly enhance virus spread, and can also provide an additional reservoir for virus persistence during anti-viral drug therapy. In addition, the initial number of HIV-specific CD4 T cells is an important determinant of acute infection dynamics. A high initial number of HIV-specific CD4 T cells can lead to a sudden and fast drop of the population of HIV-specific CD4 T cells which results quickly in their extinction. On the other hand, a low initial number of HIV-specific CD4 T cells can lead to a prolonged persistence of HIV-specific CD4 T cell help at higher levels. The model suggests that boosting the population of HIV-specific CD4 T cells can increase the amount of virus-induced immune impairment, lead to less efficient anti-viral effector responses, and thus speed up disease progression, especially if effector responses such as CTL have not been sufficiently boosted at the same time.

Figure 1

Basic dynamics of HIV in specific CD4 T cells. The figure is based on model (2). It shows the outcome of infection depending on the initial conditions. (i) If the initial virus load is low, there is not enough antigenic stimulation to establish a CD4 T cell response. Consequently, infection in the specific CD4 T cell population also goes extinct. (ii) If the initial virus load is high, there is enough antigenic stimulation to establish the CD4 T cell response. Consequently, the infection is also established. The virus spreads through the entire population of HIV-specific CD4 T cells and attains 100% prevalence in this population (that is, all specific T cells are infected). Parameters were chosen as follows. r=1, d=0.1, k=10, β=0.2, a = 0.2, η=1, u=0.5. Initial conditions were as follows: x₀=0.1, y₀=0; for (i) v₀=0.1; for (2) v₀=1.

Figure 2

Virus growth and the impairment of help as a function of the initial number of specific CD4 T cells. The figure is based on model (3) which contains both specific and non-specific target cells. (i) Virus growth: The very first phase of virus growth is the same, regardless of the initial number of specific CD4 T cells. This is because the infection is initiated in the non-specific target cells and it is assumed that specific T cells only become infected after a certain time threshold, because it will take some time for virus to meet the majority of HIV-specific T cells (the virus enters through mucosal surfaces and then makes its way to the blood where the majority of T cells are located). Subsequently, the rate of virus growth depends on the initial number of specific CD4 T cells. The higher the initial number of CD4 T cells, the faster the rate of virus growth. Both time series eventually converge to the same equilibrium. The reason is that this model does not contain any immune effector responses. (ii) Dynamics of functional specific CD4 T cells. We define this population as uninfected T cells. This assumes that infected T cells are impaired. A high initial number of specific CD4 T cells results in a fast decline of functional helper cells. If the initial number of specific CD4 T cells is lower, functional helper cells remain at higher levels for a longer period of time. In the simulation shown here, the population of specific CD4 T cells actually expands before declining to low levels. Parameters were chosen as follows. λ=1, r=1, δ=0.01, α=0.2, γ=0.005, β=0.3, d=0.001, a=0.2, k=10, η=1, u=1. Initial conditions were as follows: T₀=λ/d, I₀=0.0001, x₀=0.1 or x₀=10, y₀=0, v₀=ηI₀/u.

Figure 3

CTL dynamics as a function of the initial number of specific CD4 T cells. The figure is based on models (4,5). We distinguish between two scenarios. (i) Effectors are generated at a relatively fast rate. In this case, a high initial number of specific CD4 T cells is beneficial for the immune response. (ii) Effectors are generated with a slower rate. In this case, a low initial number of specific CD4 T cells is beneficial for immunity. Parameters were chosen as follows. (i) λ=1, r=1, δ=0.01, α=0.2, γ=0.01, β=0.3, d=0.001, a=0.2, p=45, c=1, b=0.1, k=10, η=1, u=1, φ=1.5, ξ=0.01, ε=1, n=10. For (i) ρ=1 and for (ii) ρ=0.3. Initial conditions were as follows: T₀=λ/d, I₀=0.0001, x₀=0.1 or x₀=10, y₀=0, v₀=ηI₀/u, m₀=0.1, mi₀=0, w₀=0, z₀=0.

Figure 4

Virus dynamics during drug therapy. Phase of drug therapy is indicated by shading. This figure is based on model (3), and does not include immune effectors for simplicity. We make the extreme assumption that there are no latently infected cells and that therapy can eradicate HIV from the non-specific target cells. We do this in order to demonstrate that the auto-catalytic virus spread via antigen driven division of specific CD4 T cells can present a separate reservoir which can prevent eradication of HIV from the host. Upon treatment, virus load is significantly reduced in the simulation, but settles at a persistent equilibrium. This happens, although the number of infected non-specific target cells is eradicated, and no latency is assumed. The key lies in the dynamics of the infected specific CD4 T cells. Because drug therapy reduces infectious spread of HIV, the amount of antigenic stimulation is reduced. Consequently, the number of infected specific helper cells declines. However, this population of infected specific helper cells provides enough antigenic stimulation itself to keep these cells dividing. Because drug therapy does not inhibit the mitotic spread of the infection, the virus persists. Parameters were chosen as follows. λ=100, r=1.5, δ=0.01, α=0.3, γ=0.01, β=0.3, d=0.01, a=0.3, k=1, η=1, u=1. During treatment, γ=β=0. Regarding initial conditions upon start of therapy, the system was run until all populations equilibrated, and then treatment was initiated in the model.