Stochastic microbiome assembly depends on context

Proc Natl Acad Sci U S A. 2022 Feb 15;119(7):e2115877119. doi: 10.1073/pnas.2115877119.

Abstract

Observational studies reveal substantial variability in microbiome composition across individuals. Targeted studies in gnotobiotic animals underscore this variability by showing that some bacterial strains colonize deterministically, while others colonize stochastically. While some of this variability can be explained by external factors like environmental, dietary, and genetic differences between individuals, in this paper we show that for the model organism Drosophila melanogaster, interactions between bacteria can affect the microbiome assembly process, contributing to a baseline level of microbiome variability even among isogenic organisms that are identically reared, housed, and fed. In germ-free flies fed known combinations of bacterial species, we find that some species colonize more frequently than others even when fed at the same high concentration. We develop an ecological technique that infers the presence of interactions between bacterial species based on their colonization odds in different contexts, requiring only presence/absence data from two-species experiments. We use a progressive sequence of probabilistic models, in which the colonization of each bacterial species is treated as an independent stochastic process, to reproduce the empirical distributions of colonization outcomes across experiments. We find that incorporating context-dependent interactions substantially improves the performance of the models. Stochastic, context-dependent microbiome assembly underlies clinical therapies like fecal microbiota transplantation and probiotic administration and should inform the design of synthetic fecal transplants and dosing regimes.

Keywords: Drosophila; community assembly; ecological interactions; microbiome.

MeSH terms

  • Animals
  • Bacteria / classification*
  • Bacterial Physiological Phenomena / genetics
  • Drosophila melanogaster / microbiology*
  • Microbiota*
  • Models, Biological
  • Species Specificity
  • Stochastic Processes

Associated data

  • Dryad/10.5061/dryad.2sr6316