Strain dropouts reveal interactions that govern the metabolic output of the gut microbiome

Abstract

The gut microbiome is complex, raising questions about the role of individual strains in the community. Here, we address this question by focusing on a functional unit within the community, the metabolic niche that controls bile acid 7-alpha-dehydroxylation. By constructing variants of a complex defined community in which we drop out strains that occupy this niche, we explore how interactions within and between niches shape community-level metabolism. Omitting both members of the niche, Clostridium scindens (Cs) and Clostridium hylemonae (Ch), eliminates secondary bile acid production and reshapes the community in a highly specific manner: eight strains go up or down in relative abundance by >100-fold, while the remaining strains are largely unaffected. In single-strain dropout communities (i.e., a strain swap within the niche), Cs and Ch reach the same relative abundance and dehydroxylate bile acids to a similar extent. However, the effect on strains in other niches differs markedly: Clostridium sporogenes increases >1000-fold in the delta-Cs but not delta-Ch dropout, reshaping the pool of microbiome-derived phenylalanine metabolites. Thus, strains that are functionally redundant within a niche can have widely varying impacts outside the niche, and a strain swap can ripple through the community in an unpredictable manner, resulting in a large impact on an unrelated community-level phenotype. Mice colonized by the delta-Cs/delta-Ch community show decreased liver steatosis relative to those colonized by the delta-Ch community, demonstrating that a single strain from the microbiome can have a substantive impact on host physiology. Our work opens the door to the mechanistic studies of the role of an individual strain on community ecology and host physiology.

Nutritional and host environments determine community ecology and keystone species in a synthetic gut bacterial community

“…Omics-based approaches have limited traceability of individual community members and experimental means to mechanistically resolve interaction networks. Complementary, the use of synthetic communities provides a reductionist tool to mechanistically resolve interspecies interactions and identify keystone species (17,18,36). Here, we explored the OMM 12 interaction network using single-strain dropout communities across various culture media conditions and in gnotobiotic mice.…”

“…For this purpose, synthetic model communities are indicated, where members are well-characterized, interactions can be experimentally determined and hypotheses can be verified in a systematic way. These experimental model systems also enable the identification of community members with special importance for the ecosystem, i.e., keystones in Paine’s sense (16), by allowing the implementation of systematic presence-absence studies (17, 18). Microbial keystone taxa are highly connected taxa, which have a major influence on microbiome composition and function at a particular space or time (19).…”

Gut microbiota gate host exposure to metabolites from dietary Solanums

“…One possible motive for commensal metabolism of SGAs is access to the liberated sugars as a competitive advantage in a community context; gut commensals are known to harvest sugars from plant glycosides for use as carbon sources 43 . Regardless, identification of the strains involved in SGA deglycosylation enables further investigation into the ecological and genetic bases of this metabolism, understanding of the prevalence of this metabolism across human populations, and the rational design of synthetic communities that produce a desired metabolite output 44 .…”

Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen

“…Given previous studies highlighting the immense complexity between structure-function relationships in microbial ecosystems, it may be expected that lots of high-content measurements or complex computational models trained on many parameters are necessary pre-requisites for deriving generative design principles of functional microbial communities 10, 14, 16, 44, 45 . Consistent with this notion, existing efforts have utilized several different avenues of knowledge to inform community design.…”