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Context-dependence in the symbiosis between Dictyostelium discoideum and Paraburkholderia

Citation

Scott, Trey; Queller, David; Strassmann, Joan (2022), Context-dependence in the symbiosis between Dictyostelium discoideum and Paraburkholderia, Dryad, Dataset, https://doi.org/10.5061/dryad.2ngf1vhqp

Abstract

Symbiotic interactions change with environmental context. Measuring these context-dependent effects in hosts and symbionts is critical to determining the nature of symbiotic interactions. We investigated context-dependence in the symbiosis between social amoeba hosts and their inedible Paraburkholderia bacterial symbionts, where the context is the abundance of host food bacteria. Paraburkholderia have been shown to harm hosts dispersed to food-rich environments, but aid hosts dispersed to food-poor environments by allowing hosts to carry food bacteria. Through measuring symbiont density and host spore production, we show that this food context matters in three other ways. First, it matters for symbionts, who suffer a greater cost from competition with food bacteria in the food-rich context. Second, it matters for host-symbiont conflict, changing how symbiont density negatively impacts host spore production. Third, data-based simulations show that symbiosis often provides a long-term fitness advantage for hosts after rounds of growth and dispersal in variable food-contexts, especially when conditions are harsh with little food. These results show how food context can have many consequences for the Dictyostelium-Paraburkholderia symbiosis and that both sides can frequently benefit.

Methods

We used measures of fluoresence and optical density to calculate the density of symbiotic bacteria, along with Dictyostelium discoideum spore counts and simulations in R to measure host fitness. 

Usage Notes

These data and analysis scripts can also be found at www.gitlab.com/treyjscott/farmerBH. The fitness_analysis.R script loads data, performs statistics, and creates plots used in the paper. Data sets consist of measures from after 6 days of growth (day_6_data.csv), additional days of growth (extra_day_data.csv), and a validation dataset (predicted.results.csv). Simulations can be performed by installing the farmerBH package from Gitlab (www.gitlab.com/treyjscott/farmerBH). The BHanalysis_revised05.R performs simulations as done in the paper with g = 5%. 

Funding

National Science Foundation, Award: DEB-1753743

National Science Foundation, Award: IOS-1656756