All 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.

Gut bacteria can affect key aspects of host fitness, such as development, fecundity, and lifespan, while the host, in turn, shapes the gut microbiome. However, it is unclear to what extent individual species versus community interactions within the microbiome are linked to host fitness. Here, we combinatorially dissect the natural microbiome of Drosophila melanogaster and reveal that interactions between bacteria shape host fitness through life history tradeoffs. Empirically, we made germ-free flies colonized with each possible combination of the five core species of fly gut bacteria. We measured the resulting bacterial community abundances and fly fitness traits, including development, reproduction, and lifespan. The fly gut promoted bacterial diversity, which, in turn, accelerated development, reproduction, and aging: Flies that reproduced more died sooner. From these measurements, we calculated the impact of bacterial interactions on fly fitness by adapting the mathematics of genetic epistasis to the microbiome. Development and fecundity converged with higher diversity, suggesting minimal dependence on interactions. However, host lifespan and microbiome abundances were highly dependent on interactions between bacterial species. Higher-order interactions (involving three, four, and five species) occurred in 13–44% of possible cases depending on the trait, with the same interactions affecting multiple traits, a reflection of the life history tradeoff. Overall, we found these interactions were frequently context-dependent and often had the same magnitude as individual species themselves, indicating that the interactions can be as important as the individual species in gut microbiomes.

In 1927, Steinfeld (1) reported that germ-free flies live longer than their microbially colonized counterparts, suggesting that bacteria hinder host fitness. This observation, that the microbiome can impact aging, has been replicated in flies and vertebrates (2, 3). However, a decrease in lifespan does not necessarily indicate a negative impact on the host. Organisms in their environment are selected for their fitness, which is a function of lifespan, fecundity, and development time (4). Life history tradeoffs allow local adaptation. For instance, by increasing fecundity at the expense of lifespan (5⇓–7), an organism can use either short or long generation times to achieve equal fitness. These observations set up two major questions: What is the role of an individual bacterial species versus interactions between them in determining host lifespan, and how is the microbiome effect on lifespan related to overall host fitness?

Identifying the host effects of specific bacteria has been difficult, in part, due to high gut diversity but also because interactions between bacteria can depend on context (8). Nonadditive effects of more than two variables are called higher-order interactions, and they indicate that interactions depend on context. For example, a bacterium may produce a specific B-vitamin in response to its neighbors (9, 10). This response may impact the host, and host feedbacks can mitigate or exacerbate changes in the microbial community (11). However, specific examples may be misleading, as the true complexity of a gut microbiome has never been exhaustively quantified. Thus, it remains an outstanding challenge to reverse-engineer the interaction networks that characterize microbiome/host effects relative to host interactions with individual bacterial species. Doing so would allow us to address the role of microbial community complexity in shaping host fitness. However, quantifying the set of all possible interactions of n species is a combinatorial problem involving 2n distinct bacterial communities. As n approaches the diversity of the mammalian gut with hundreds of species, this challenge becomes experimentally unfeasible.

The gut microbiome of the fruit fly Drosophila melanogaster is an effective combinatorial model because as few as five species of bacteria consistently inhabit the gut of wild and laboratory flies (12⇓–14), yielding 25 possible combinations of species. Because early work on the fruit fly microbiome suggested that it is a transient community consisting only of recently ingested bacteria (15), we set up our experiments to maintain bacterial colonization through frequent ingestion. However, newer studies demonstrate that a modified fly diet as well as specific bacterial strains make for a persistent gut microbiome (16, 17), suggesting similarities with higher organisms. Here, we isolated the five core laboratory fly gut bacteria species in culture: Lactobacillus plantarum (Lp), Lactobacillus brevis (Lb), Acetobacter pasteurianus (Ap), Acetobacter tropicalis (At), and Acetobacter orientalis (Ao). These fermentative lactic acid bacteria and acetic acid bacteria commonly occur in the wild fly gut (14, 18, 19), where they can maintain a stable association (16, 17). We constructed germ-free flies by surface-sterilizing the embryos and reinoculated the newly emerged adult flies via continuous association with defined flora using established protocols (17, 20). We made the 32 possible combinations of the five bacterial species and then quantified the microbiome composition and resultant host phenotypes of (i) development time, (ii) reproduction, and (iii) lifespan to determine the relationship between gut microbe interactions and host fitness. We tested to what extent the presence and abundance of individual bacterial species account for the fly physiology phenotypes we measured.

Finally, we introduce a mathematical framework to deconstruct microbiome/host complexity by making a conceptual analogy between the bacterial species interactions and genetic epistasis (21, 22). This approach revealed significant context-dependent interactions between two, three, four, and five species that have large impacts on host physiology, contributing to differential life history strategies.

Results

Reproduction Cannot Be Increased by Midlife Microbiome Addition. The life history tradeoff suggests that a fly born into stark conditions in the wild could maximize its fitness by first acquiring a longevity-promoting microbiome and then converting to a fecundity-promoting one when environmental conditions improve. Female flies are primarily reproductive in the first part of their life, with a gradual decay in fecundity approaching middle age (SI Appendix, Fig. S5). To test whether individual flies can switch life history strategy to match their microbiome, we aged germ-free flies for 21 d (roughly middle age) and then associated these flies with fecundity-promoting bacteria. There was no significant increase in total fecundity for these flies and a significant decrease in lifespan compared with germ-free flies (Fig. 1E; P = 0.054 for fecundity, n = 275 flies pooled across four bacterial combinations, two-sample one-sided t test; P > 0.05 for all pairwise combinations after Tukey’s multiple comparison correction; P = 2 × 10−7 for lifespan, n = 400 flies pooled across four bacterial combinations, two-sample one-sided t test; P < 0.001 for 4/4 combinations after Tukey’s correction, n = 100 flies per combination, two-sample one-sided t tests). These results are consistent with the simple hypothesis that a fly’s reproductive window cannot be extended by late-life improvement in nutrition.

Microbiome Interactions Can Change Host Physiology. We hypothesized that the microbiome may shorten lifespan through a process independent of reproduction. To examine this hypothesis, we used antibiotics to remove the microbiome of high-fecundity female flies and measured the resulting change in lifespan. We first allowed female flies with high-fecundity microbiomes to reproduce for 21 d (to a level greater than the total lifetime fecundity of germ-free flies; SI Appendix, Fig. S5), and we subsequently eliminated the microbiome using an antibiotic mixture (ampicillin, tetracycline, rifamycin, and streptomycin). In general, the midlife elimination of gut flora lengthened the female fly lifespan by roughly 15% compared with flies continuously fed live bacteria (Fig. 1E; P = 9 × 10−7, n = 560 flies pooled across bacterial combinations; P < 0.05 for four of seven combinations after Tukey’s correction for multiple pairwise comparisons, n = 80 flies per combination, two-sample one-sided t test). Total fecundity decreased slightly (Fig. 1E; P = 0.01, n = 560 flies pooled across bacterial combinations; P > 0.05 for all seven combinations after Tukey’s correction for multiple pairwise comparisons, n = 80 flies per combination, two-sample one-sided t test). This result demonstrates that the life history tradeoff is not necessarily fixed and suggests that the fly lifespan is shortened by some aspect of the bacteria rather than by reproduction. However, two specific bacterial combinations yielded no increase in lifespan when removed from their host by antibiotics: Ao and Lp+Lb+Ao, suggesting a memory in host physiology induced by these two combinations. Interestingly, neither the intermediate microbiome composition, Lp+Ao, nor the similar composition, Lp+At+Ao, (Fig. 1E; with antibiotic elimination of the microbiota-extending lifespan) showed this memory, suggesting specificity of the microbiome composition in this metabolic memory. These experiments demonstrate that interactions between bacteria can significantly impact the host’s ability to adjust its physiology.