The Use of Defined Microbial Communities To Model Host-Microbe Interactions in the Human Gut

Abstract

SUMMARYThe human intestinal ecosystem is characterized by a complex interplay between different microorganisms and the host. The high variation within the human population further complicates the quest toward an adequate understanding of this complex system that is so relevant to human health and well-being. To study host-microbe interactions, defined synthetic bacterial communities have been introduced in gnotobiotic animals or in sophisticatedin vitrocell models. This review reinforces that our limited understanding has often hampered the appropriate design of defined communities that represent the human gut microbiota. On top of this, some communities have been applied toin vivomodels that differ appreciably from the human host. In this review, the advantages and disadvantages of using defined microbial communities are outlined, and suggestions for future improvement of host-microbe interaction models are provided. With respect to the host, technological advances, such as the development of a gut-on-a-chip system and intestinal organoids, may contribute to more-accuratein vitromodels of the human host. With respect to the microbiota, due to the increasing availability of representative cultured isolates and their genomic sequences, our understanding and controllability of the human gut “core microbiota” are likely to increase. Taken together, these advancements could further unravel the molecular mechanisms underlying the human gut microbiota superorganism. Such a gain of insight would provide a solid basis for the improvement of pre-, pro-, and synbiotics as well as the development of new therapeutic microbes.

Strain-level epidemiology of microbial communities and the human microbiome

“…Primary samples can be characterized as an entire community via gnotobiotics [186,187] or continuous culture [188,189], or individual isolate strains grown, characterized, or (when possible) genetically manipulated [15,190,191]. Such approaches dovetail nicely with "bottom up" approaches (analogous to reverse genetics) that identify and characterize healthrelevant strains by directly beginning with isolates and assessing their phenotypes in gnotobiotic mono-or combinatorial colonization [192][193][194][195][196][197] or, when possible, human feeding [198][199][200] or microbiota transplant clinical trials [201][202][203][204][205].…”

Contrasting Strategies: Human Eukaryotic Versus Bacterial Microbiome Research

“…; Elzinga et al. ; Vega and Gore ). Representative synthetic communities can be used to isolate hypothesized organizational and causal relationships (e.g.…”

“…Eukaryotic microbiome research would also benefit from intermediate methods that fall between the complex communities described by detailed microbiome analyses and more abstract mathematical and computational models. Synthetic microbial communities are another tool being developed for bacterial microbiome research in order to understand microbial communities from the "bottom up" (De Roy et al 2014;Dolin sek et al 2016;Elzinga et al 2019;Vega and Gore 2018). Representative synthetic communities can be used to isolate hypothesized organizational and causal relationships (e.g.…”

Multi-Omics Approaches: The Key to Improving Respiratory Health in People With Cystic Fibrosis?

“…This could provide specific information on bacterial metabolite usage, enzyme induction, regulation of inflammatory markers, secondary metabolism, the efficacy and unintended consequences of administered drugs and may reveal components of the microbiota that could be disrupted or selectively "drugged" (Wallace and Redinbo, 2013). Synthetic systems models are now being developed to study host-microbe interactions (Elzinga et al, 2019), an important step due to the growing appreciation that the gut microbiota and its metabolites can influence and modulate host immune function (Li and Somerset, 2018) and that these metabolites are present in distal organs such as the lungs (Schroeder and Bäckhed, 2016).…”