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

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article


Microbiomes perform critical functions across many environments on Earth 1–3 . However, elucidating principles of their design is immensely challenging 4–7 . Using a diverse bank of human gut commensal strains and clearance of multi-drug resistant Klebsiella pneumoniae as a target, we engineered a functional synthetic microbiome using a process that was agnostic to mechanism of action, bacterial interactions, or compositions of natural microbiomes. Our strategy was a modified ‘Design-Build-Test-Learn’ approach (‘DBTL+’) coupled with statistical inference that learned design principles by considering only the strain presence-absence of designed communities. In just a single round of DBTL+, we converged on a generative model of K. pneumoniae suppression. Statistical inference performed on our model identified 15 strains that were key for community function. Combining these strains into a community (‘SynCom15’) suppressed K. pneumoniae across unrelated in vitro environments and matched the clearance ability of a whole stool transplant in a pre-clinically relevant mouse model of infection. Considering metabolic profiles of communities instead of strain presence-absence yielded a poor generative model, demonstrating the advantage of using strain presence-absence for deriving principles of community design. Our work introduces the concept of ‘statistical design’ for engineering synthetic microbiomes, opening the possibility of synthetic ecology more broadly.

Article activity feed