Market forces determine the distribution of a leaky function in a simple microbial community

Many biological functions are leaky, and organisms that perform them contribute some of their products to a community "marketplace" where non-performing individuals may compete for them. Leaky functions are partitioned unequally in microbial communities, and the evolutionary forces determining which species perform them and which become beneficiaries are poorly understood. Here we demonstrate that the market principle of comparative advantage determines the distribution of a leaky antibiotic resistance gene in an environment occupied by two "species" - strains of Escherichia coli growing on mutually exclusive resources and thus occupying separate niches. Communities comprised of antibiotic-resistant cells were rapidly invaded by sensitive cells of both types. While the two phenotypes coexisted stably for 500 generations, in 15/18 replicates, antibiotic sensitivity became fixed in one species. Fixation always occurred in the same species, despite both species being genetically identical except for their niche-defining mutation. In the absence of antibiotic, the fitness cost of resistance was identical in both species. However, the intrinsic resistance of the species that ultimately became the sole helper was significantly lower, and thus its reward for expressing the resistance gene was higher. Opportunity cost of resistance, not absolute cost or efficiency of antibiotic removal, determined which species became helper, consistent with the economic theory of comparative advantage. We present a model that suggests that this market-like dynamic is a general property of Black Queen systems, and in communities dependent on multiple leaky functions, could lead to the spontaneous development of an equitable and efficient division of labor.A readme file is included with the dataset. We are happy to assist you in using the data if you would like to replicate our findings -- contact us at evolve@uab.edu. Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DBI-0939454Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1450078Funding provided by: Simons FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000893Award Number: Early Career FellowshipThis dataset contains raw data from microbial competition experiments; growth curve parameters extracted from microbial growth experiments; and R code used to analyze these data statistically as well as generate the figures contained in the manuscript. They have been minimally processed, with the exception of the growth curve experiments, where the raw output from the automated plate reader has been reduced to "area under the curve" and "exponential growth rate" parameters prior to analysis