SignificanceAll animals have associated microbial communities called microbiomes that influence the physiology and fitness of their host. It is unclear to what extent individual microbial species versus interactions between them influence the host. Here, we mapped all possible interactions between individual species of bacteria against Drosophila melanogaster fruit fly fitness traits. Our approach revealed that the same bacterial interactions that shape microbiome abundances also shape host fitness traits. The fitness traits of lifespan and fecundity showed a life history tradeoff, where equal total fitness can be gotten by either high fecundity over a short life or low fecundity over a long life. The microbiome interactions are as important as the individual species in shaping these fundamental aspects of fly physiology.

Abstract:With hundreds of species interacting with each other as well as with specific proteins and cells in the body, the gut microbiome is a complex ecosystem embedded within a complex organism. Microbiome impacts on host health can shape key aspects of fitness, such as development, 1 fecundity, 2 and lifespan, 3,4 while the host in turn can shape the microbiome. 5 However, complex interactions between microbes can make downstream effects unpredictable, such as when toxin-producing Clostridium species cause pathogenesis after antibiotics reduce gut diversity. 6 A pressing need exists to deconstruct the effects of gut diversity on host health, and new mathematical approaches are needed to quantify the complex patterns of interactions. Central to the microbiome-host relationship are questions of how bacterial diversity is maintained in the gut 7 and how this diversity impacts host fitness. 8 Here we show that interactions between bacteria are major determinants of host physiology and the maintenance of bacterial diversity. We performed a complete combinatorial dissection of the naturally low-diversity Drosophila gut microbiome using germ-free flies colonized with each possible combination of five core species of bacteria, forming a five-dimensional landscape in ecological state space. For each species combination, we measured the resulting bacterial community abundances and fly fitness traits including (i) development, (ii) reproduction, and (iii) lifespan. Notably, we found that the fly gut environment promotes bacterial diversity, which in turn accelerates development, reproduction, and aging. From these measurements we calculated the impact of bacterial interactions on fly fitness by adapting a combinatorial geometry approach, 9 to the microbiome. 10 We found that host phenotypes (e.g. lifespan) from single bacterial species are not predictive of host phenotypes in diverse communities. By contrast, higher-order interactions (involving 3, 4, and 5 species) are widely prevalent and impact both host physiology and the maintenance of bacterial diversity, as recently predicted by ecologists. 11 With regard to evolution, the impacts of bacterial interactions on gut community composition parallel the impacts on host fitness traits, providing a feedback that, propagated over time, may poise a population for emergence of co-evolving microbiome-host units.peer-reviewed)

The concept of genetic epistasis defines an interaction between two genetic loci as the degree of non-additivity in their phenotypes. A fitness landscape describes the phenotypes over many genetic loci, and the shape of this landscape can be used to predict evolutionary trajectories. Epistasis in a fitness landscape makes prediction of evolutionary trajectories more complex because the interactions between loci can produce local fitness peaks or troughs, which changes the likelihood of different paths. While various mathematical frameworks have been proposed to calculate the shapes of fitness landscapes, Beerenwinkel, Pachter and Sturmfels (2007) suggested studying regular subdivisions of convex polytopes. In this sense, each locus provides one dimension, so that the genotypes form a cube with the number of dimensions equal to the number of genetic loci considered. The fitness landscape is a height function on the coordinates of the cube. Here, we propose cluster partitions and cluster filtrations of fitness landscapes as a new mathematical tool, which provides a concise combinatorial way of processing metric information from epistatic interactions. Furthermore, we extend the calculation of genetic interactions to consider interactions between microbial taxa in the gut microbiome of Drosophila fruit flies. We demonstrate similarities with and differences to the previous approach. As one outcome we locate interesting epistatic information on the fitness landscape where the previous approach is less conclusive.