Deciphering interactions between the gut microbiota and the immune system via microbial cultivation and minimal microbiomes

Abstract

The community of microorganisms in the mammalian gastrointestinal tract, referred to as the gut microbiota, influences host physiology and immunity. The last decade of microbiome research has provided significant advancements for the field and highlighted the importance of gut microbes to states of both health and disease. Novel molecular techniques have unraveled the tremendous diversity of intestinal symbionts that potentially influence the host, many proof-of-concept studies have demonstrated causative roles of gut microbial communities in various pathologies, and microbiome-based approaches are beginning to be implemented in the clinic for diagnostic purposes or for personalized treatments. However, several challenges for the field remain: purely descriptive reports outnumbering mechanistic studies and slow translation of experimental results obtained in animal models into the clinics. Moreover, there is a dearth of knowledge regarding how gut microbes, including novel species that have yet to be identified, impact host immune responses. The sheer complexity of the gut microbial ecosystem makes it difficult, in part, to fully understand the microbiota-host networks that regulate immunity. In the present manuscript, we review key findings on the interactions between gut microbiota members and the immune system. Because culturing microbes allows performing functional studies, we have emphasized the impact of specific taxa or communities thereof. We also highlight underlying molecular mechanisms and discuss opportunities to implement minimal microbiome-based strategies.

Keywords: altered Schaedler flora; anaerobic cultivation; gut microbiota; immune system; lipids; minimal microbiomes.

FIGURE 1

From complex gut microbial community to defined experimental systems. Prerequisite for the workflows presented here is an animal model (a mouse in the present example) with a specific phenotype (e.g. chronic disease with a clear measurable endpoint) or a physiological parameter of interest (e.g. maturation of specific immune cell populations). The phenotype/parameter of interest can be associated with defined gut microbiota signatures and must be absent in germfree animals. Starting from a gut sample collected from an animal with the specific feature of interest, two routes of action can be followed: (A) The classical route is based on cultivation and isolation of strains (e.g. microbial colonies on nutrient agar) with the aim of identifying single components of the complex ecosystem of origin that can drive the phenotype. Each isolate is characterized taxonomically and functionally and deposited in public culture collections for downstream access. Based on expert knowledge or assisted by computational methods, single strains are combined together and used to colonize germfree animals with the goal of mimicking the disease phenotype or physiological parameter of interest. (B) A second, less explored route is the selection of microbial communities with the aim of obtaining the most reduced complexity still associated with the phenotype of interest. This can be achieved via iterative association of germfree animals (circled black arrow) in the presence of selective forces (e.g. special diet or antibiotics), and/or in vitro enrichment of specific microbes (e.g. via selective media or simply by serial dilution) prior to colonization of germfree animals. The advantage of performing an in vitro step prior to association with mice is bulk selection of microorganisms that can indeed grow under laboratory conditions, which can substantially facilitate downstream identification of single members of the final minimal microbiome. The progress of community reduction can be monitored throughout the workflow using targeted (16S rRNA amplicon) or shotgun metagenomic sequencing. Of note, workflow B can be followed by A (dashed arrow) for detailed characterization of single community members. The new gnotobiotic models created by either approach can ultimately be used for detailed experimental testing of microbe-microbe and microbe-host interactions.