Microbiota-targeted therapies: an ecological perspective

Abstract

The connection between disease and the disruption of homeostatic interactions between the host and its microbiota is now well established. Drug developers and clinicians are starting to rely more heavily on therapies that directly target the microbiota and on the ecology of the microbiota to understand the outcomes of these treatments. The effects of those microbiota-targeted therapies that alter community composition range in scale from eliminating individual strains of a single species (for example, with antibacterial conjugate vaccines) to replacing the entire community with a new intact microbiota (for example, by fecal transplantation). Secondary infections linked to antibiotic use provide a cautionary tale of the unintended consequences of perturbing a microbial species network and highlight the need for new narrow-spectrum antibiotics with rapid companion diagnostics. Insights into microbial ecology will also benefit the development of probiotics, whose therapeutic prospects will depend on rigorous clinical testing. Future probiotics may take the form of a consortium of long-term community residents: "a fecal transplant in a capsule." The efficacy of microbiota-targeted therapies will need to be assessed using new diagnostic tools that measure community function rather than composition, including the temporal response of a microbial community to a defined perturbation such as an antibiotic or probiotic.

Fig. 1

Microbial communities as networks. (A) Shown are three types of perturbation to a network of microbial species such as that found in the microbiota at various body sites. The microbiota can be perturbed by excision of a single species (node) by a vaccine or a species-specific antibiotic, by elimination of multiple nodes or a subnetwork by an antibiotic, or by replacement of a whole network using microbiota transplantation. (B) Two ways of modeling a microbial community as a network. (Left) Nodes as species, and edges as interactions among species. Species networks can be constructed directly from metagenomic sequence data, but they lack functional information. (Right) Nodes as functions, and edges as interactions among functions. Function networks can generate hypotheses about the mechanism of microbiota-host interactions, but they require mapping genes to functions or a panel of direct functional measurements.

Fig. 2

New opportunities in treatment and diagnostics. (A) Antibiotics save countless lives, but when they kill mutualistic (that is, helpful) microbiota that normally check the growth of pathogens, a secondary infection can ensue. Repopulating antibiotic-treated patients with probiotics is a promising strategy to prevent secondary infections. (B) Given that the human microbiota has many normal taxonomic compositions, it might be easier to develop markers of a normal or healthy community in terms of functional attributes like resistance and resilience. A single time-point measurement of taxon and gene abundance (left) is limited in its ability to provide functional information. Diagnostics based on direct measurements of metabolites (right, top) and temporal measurements of robustness to antibiotic or probiotic challenge (right, bottom) would enable community function to be assessed directly.

Fig. 3

Microbiota-targeted therapy can shift a community to a healthier stable state. A microbial community’s response to a microbiota-targeted therapy can be illustrated with a stability landscape diagram (). The ball containing the network represents the microbial community, and the shift in its horizontal position within the landscape represents movement between alternative stable states. The depth of a basin indicates the probability that the community will stay in that specific state in response to perturbation and, therefore, reflects the degree of perturbation needed to shift the community to an alternative stable state, for example, from state 1 to state 2. In this illustration, a therapeutic perturbation that removes some nodes (yellow and green) from the community network is sufficient to shift the community to an alternative, and in this case healthier, stable state (state 1).