Parasite resource manipulation drives bimodal variation in infection duration

Abstract

Over a billion people on earth are infected with helminth parasites and show remarkable variation in parasite burden and chronicity. These parasite distributions are captured well by classic statistics, such as the negative binomial distribution. But the within-host processes underlying this variation are not well understood. In this study, we explain variation in macroparasite infection outcomes on the basis of resource flows within hosts. Resource flows realize the interactions between parasites and host immunity and metabolism. When host metabolism is modulated by parasites, we find a positive feedback of parasites on their own resources. While this positive feedback results in parasites improving their resource availability at high burdens, giving rise to chronic infections, it also results in a threshold biomass required for parasites to establish in the host, giving rise to acute infections when biomass fails to clear the threshold. Our finding of chronic and acute outcomes in bistability contrasts with classic theory, yet is congruent with the variation in helminth burdens observed in human and wildlife populations.

Keywords: acute; chronic; immunity; parasite; resource manipulation; within-host.

Figure 1.

Parasite biomass and induced immune response in time series starting from various initial conditions. Simulation of infections starting at eight different doses (a,c), and starting at different host food availability levels (b,d). σ_C = 0.52, other parameters have default values (electronic supplementary material, table S1). Only the first 500 time steps of the integration are shown, see electronic supplementary material, figure S4 for the extended time series with full transient dynamics up to the attractor states and for the dynamics of other system variables. (Online version in colour.)

Figure 2.

Distribution of parasite burdens under different scenarios. For each scenario, 1000 hosts were infected with a parasite dose randomly drawn from a normal distribution with mean of 3 × 10⁻⁴ g and standard deviation of 8 × 10⁻⁵ g. With the default value σ_C = 0.52, this distribution centres around the unstable equilibrium in figure 3c. All parameters have default values (electronic supplementary material, table S1); to convert biomass into parasite numbers, we used the mass of an adult worm of 5 × 10⁻⁵ g. First scenario: every host is sampled at the same infection age (top row). Second scenario: each host is sampled at a different infection age, as in sampling a natural population (second row), which was repeated three times to simulate sampling three different populations. Third scenario: σ_C = 0.45, only acute infections are possible (third row). Last scenario: σ_C = 0.9, only chronic infections are possible (bottom row). Legend in panels: AIC scores from fitting different statistical distributions to distribution data using the ‘fitdistr’ function (MASS package in R; black—negative binomial, red—normal, blue—Poisson). (Online version in colour.)

Figure 3.

Bistability with acute and chronic infections. Within-host biomass dynamics as a function of parasite attack rate (resource uptake rate). Low attack rate values (0 < σ_C < 0.5) exclusively allow for acute infections (no equilibrium with positive parasite biomass, a,c left of PT). High attack rate values (σ_C > 0.89) exclusively allow for chronic infections (only an equilibrium with high parasite biomass, b,c, right of IT). Intermediate attack rate values result in bistability between acute and chronic infection states, meaning that both the state with extinct parasite (acute) and high parasite biomass (chronic) are stable equilibria. The relationship between time series (a,b) and equilibrium solutions (c–f) is shown. Time series (a,b) illustrate the induction and waning of immune response in an acute infection (a) and the elevated endpoint in a chronic infection (b). Grey shaded area: bistable region, enclosed between the persistence threshold (PT) and invasion threshold (IT) (see electronic supplementary material). Bold, grey arrows (c) indicate dynamics in time as approaching equilibria. F = 1.5, ε_Amin = 0.4 (compare with electronic supplementary material, figure S7); other parameters have default values. (Online version in colour.)