The diversity and abundance of bacteria within an animal host can influence many important phenotypes of the host including obesity, immunity, and mating behavior. Conversely, the host regulates abundance as well as diversity of the microbes. While there is great interest in basic ecological mechanisms structuring within-host microbial populations and communities, an explicit test of traditional ecological theories is rare. Specifically, a major challenge in gut microbiota research is to infer the ecological processes shaping the microbial communities from commonly collected datasets: fecal microbial abundance time-series data. Here, we present a theoretical framework that explicitly links within-host processes to measurable statistics from fecal time-series data. We then apply this framework to Drosophila melanogaster – Acetobacter tropicalis model system, and test how host-microbial interaction changes under 2-by-2 treatments. Axenic or gnotobiotic D. melanogaster hosts are exposed to a mixture of fluorescent microspheres and low or high density of A. tropicalis, a key D. melanogaster gut symbiont. Our fly treatments test for effects of factors such as empty gut niches in axenic flies, altered gut morphology in gnotobiotic flies, and differential immune responses. The microbial density treatments test for effects of factors such as quorum sensing and threshold effects for immune responses.
We calculate the theoretical mean and variance of the microbial exit time from ordinary differential equation models. Our models show that changes in within-host ecological processes lead to measurable differences in fecal time-series statistics. We generalize the models to show that the same qualitative result holds for large class of models.
Our experiments show that axenic flies exposed to high density bacteria lead to significantly different response, compared to the other three treatments. We hypothesize that the gut is easier to colonize, or immune response is overexpressed with high microbial population size. Our experiments also show that specialization along Drosophila gut compartments may have major impacts not only on host physiology, but also on microbial dispersal ecology and the resulting host-microbe interaction.
Temporal dynamics compliment static patterns and lead to new insights in gut microbial ecology. Importantly, our approach only relies on time-series data without any knowledge on the abundance of ingested bacteria. Our framework could thus be applied to field data as well as other systems such as human microbiome project. Furthermore, the method allows us to investigate how microbial interactions (e.g. competition) may lead to differential population dynamics and coexistence in the host.