Synthetic approaches help to dissect the complexity of microbial interactions.
Microorganisms are present in nearly every niche on Earth, from the human gut to the deep ocean, but it is estimated that only 1% of environmental bacteria are cultivatable. The inability to culture the majority of microbial species has motivated the use of culture-independent methods for studies of microbial diversity, composition, and distribution in different environments. Such methods range from sequencing to imaging techniques such as fluorescence in situ hybridization to mass spectrometry, and have offered metabolic, taxonomic, and spatial profiles of microbial communities.
However, mechanistic understanding of how microbes interact with each other and with their environment has lagged behind. Deconvolution of the sheer complexity of microbial interactions in natural environments has been a major challenge.
Synthetic microbial communities offer reduced complexity and are amenable to mathematical modeling, which makes them a promising resource for studying interactions and their contribution to community structure and function. To this end, advances in synthetic biology have made it possible to engineer microbes with genetically defined properties. These engineered bacteria have been combined with artificial environments for the study of microbial interactions in response to environmental cues.
In most cases, synthetic approaches involve the coculture of combinations of well-characterized bacterial strains. Although the approach seems straightforward in principle, interactions may be much more complex than simple cooperative associations. Therefore, mathematical modeling is often used to describe and predict these interactions. For example, a constant yield expectation analytical model is used to deconvolute metabolic interactions in coculture by leveraging the metabolic profiles of monocultures and cocultured strain pairs (Cell Syst. 7, 245–257; 2018).
Experimentally, microchamber-based methods have been developed to mimic the natural environment in which nutrient exchange, water flow, and exposure to oxygen can be crucial for accurate profiling of the microbial community. For example, a microfluidic assay enables assessment of dynamic root–microbe interactions (Proc. Natl Acad. Sci. USA 114, 4549–4554; 2017), and an in situ chemotaxis assay (ISCA) accesses marine microbial behaviors at spatially relevant scales (Nat. Microbiol. 2, 1344–1349; 2017).
Despite the remarkable advances in the study of microbial communities, we are still far from understanding the full picture. Moving beyond purely observational approaches, we look forward to experimental and computational methods that facilitate the interpretation of microbes’ interactions with each other and with their communities.