Immunological feedback loops generate parasite persistence thresholds that explain variation in infection duration

Abstract

Infection duration affects individual host fitness and between-host transmission. Whether an infection is cleared or becomes chronic depends on the complex interaction between host immune responses and parasite growth. Empirical and theoretical studies have suggested that there are critical thresholds of parasite dose that can determine clearance versus chronicity, driven by the ability of the parasite to manipulate host immunity. However, the mammalian immune response is characterized by strong positive and negative feedback loops that could generate duration thresholds even in the absence of direct immunomodulation. Here, we derive and analyse a simple model for the interaction between T-cell subpopulations and parasite growth. We show that whether an infection is cleared or not is very sensitive to the initial immune state, parasite dose and strength of immunological feedbacks. In particular, chronic infections are possible even when parasites provoke a strong and effective immune response and lack any ability to immunomodulate. Our findings indicate that the initial immune state, which often goes unmeasured in empirical studies, is a critical determinant of infection duration. This work also has implications for epidemiological models, as it implies that infection duration will be highly variable among individuals, and dependent on each individual's infection history.

Keywords: Allee effect; immunity; infection duration.

Figure 1.

Phase portraits for the dimensionless immune model (equation 2.1) as immune stimulation increases across the columns. Across the top row, ${\displaystyle {\beta }_1={\beta }_2=0.05\left(\mathit{a}\right),0.08\left(\mathit{b}\right),0.11\left(\mathit{c}\right)\mathrm{a}\mathrm{n}\mathrm{d}0.14\left(\mathit{d}\right)}$ ; across the bottom row, ${\beta }_2=2{\beta }_1$ , with ${\displaystyle {\beta }_1=0.04\left(\mathit{e}\right),0.06\left(\mathit{f}\right),0.07\left(\mathit{g}\right)\mathrm{a}\mathrm{n}\mathrm{d}0.075\left(\mathit{h}\right)}$ . Nullclines are shown by black and red lines. Nullcline intersections represent equilibria. Stable nodes are indicated by filled-in triangles, with the colour indicating the immunological state (as in figure 2); unstable nodes are indicated by hollow triangles; unstable saddles are indicated by hollow diamonds.

Figure 2.

Infection outcomes as the initial immune state varies across values of χ1, χ2 and ${\iota }_{12}={\iota }_{21}$ . For the ‘high Th2’ outcome, the immune system is polarized towards a Th2 response and the parasite is extinct (acute infection). For the ‘high Th1+2’ outcome, there is a high level of both Th1 and Th2 immune cells and the parasite biomass is very low. For the ‘high Th1’ outcome, the immune system is polarized towards a Th1 response and parasite biomass is very high (chronic infection). For the ‘low Th1+2’ outcome, there is a low level of both Th1 and Th2 immune cells and the parasite biomass is high.

Figure 3.

Variation in the infection outcome across initial parasite doses can reveal underlying immune mechanisms. (a) If the parasite activates a strong and Th2-biased immune response, it will be cleared (black area) regardless of the initial immune state or dose ( χ2=1, χ1=0.04,ιij=1 ). Asterisks show the initial parameter values for the numerical simulation results shown in the rightmost columns. (b) If cross-inhibition is relatively strong, then even low doses of the parasite can lead to a chronic infection if initial Th1 bias is sufficiently high (blue area) whereas high doses are cleared across a wider range of initial Th1 biases (black area) ( χ2=0.155, χ1=0.067,ιij=8 ). (c) If the parasite manipulates the immune system by activating the inappropriate Th1 response, then any dose can be cleared if initial Th1 bias is sufficiently low (black area) whereas high doses become chronic across a wider range of initial Th1 biases (red area) ( χ2=0.04, χ1=1,ιij=1) .