A microbial mutualist within host individuals increases parasite transmission between host individuals: Evidence from a field mesocosm experiment

The interactions among host-associated microbes and parasites can have clear consequences for disease susceptibility and progression within host individuals. Yet, empirical evidence for how these interactions impact parasite transmission between host individuals remains scarce. We address this scarcity by using a field mesocosm experiment to investigate the interaction between a systemic fungal endophyte, Epichloe coenophiala, and a fungal parasite, Rhizoctonia solani, in leaves of a grass host, tall fescue. Specifically, we investigated how this interaction impacted parasite transmission under field conditions in replicated experimental host populations. Epichloe-inoculated populations tended to have greater disease prevalence over time, though this difference had weak statistical support. More clearly, Epichloe-inoculated populations experienced higher peak parasite prevalences than Epichloe-free populations. Epichloe conferred a benefit in growth; Epichloe-inoculated populations had greater aboveground biomass than Epichloe-free populations. Using biomass as a proxy, host density was correlated with peak parasite prevalence, but Epichloe still increased peak parasite prevalence after controlling for the effect of biomass. Together, these results suggest that within-host microbial interactions can impact disease at the population level. Further, while Epichloe is clearly a mutualist of tall fescue, it may not be a defensive mutualist in relation to R. solani.

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Linking Bioenergetics and Parasite Transmission Models Suggests Mismatch Between Snail Host Density and Production of Human Schistosomes

Integrative and Comparative Biology ◽

10.1093/icb/icz058 ◽

2019 ◽

Vol 59 (5) ◽

pp. 1243-1252 ◽

Cited By ~ 3

Author(s):

Matthew Malishev ◽

David J Civitello

Keyword(s):

Host Density ◽

Population Level ◽

Immune Defense ◽

Parasite Transmission ◽

Host Snail ◽

Individual Level ◽

High Resource ◽

Parasite Ecology ◽

The Individual ◽

Host Parasite

Abstract The consequences of parasite infection for individual hosts depend on key features of host–parasite ecology underpinning parasite growth and immune defense, such as age, sex, resource supply, and environmental stressors. Scaling these features and their underlying mechanisms from the individual host is challenging but necessary, as they shape parasite transmission at the population level. Translating individual-level mechanisms across scales could inherently improve the way we think about feedbacks among parasitism, the mechanisms driving transmission, and the consequences of human impact and disease control efforts. Here, we use individual-based models (IBMs) based on general metabolic theory, Dynamic Energy Budget (DEB) theory, to scale explicit life-history features of individual hosts, such as growth, reproduction, parasite production, and death, to parasite transmission at the population level over a range of resource supplies focusing on the major human parasite, Schistosoma mansoni, and its intermediate host snail, Biomphalaria glabrata. At the individual level, infected hosts produce fewer parasites at lower resources as competition increases. At the population level, our DEB–IBM predicts brief, but intense parasite peaks early during the host growth season when resources are abundant and infected hosts are few. The timing of these peaks challenges the status quo that high densities of infected hosts produce the highest parasite densities. As expected, high resource supply boosts parasite output, but parasite output also peaks at modest to high host background mortality rates, which parallels overcompensation in stage-structured models. Our combined results reveal the crucial role of individual-level physiology in identifying how environmental conditions, time of the year, and key feedbacks within host–parasite ecology interact to define periods of elevated risk. The testable forecasts from this physiologically-explicit epidemiological model can inform disease management to reduce human risk of schistosome infection.

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