Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen
Engineering functional microbiomes is challenging due to complex interactions between bacteria and their environments1-6. Using a set of 848 gut commensal strains and clearance of multi-drug resistant Klebsiella pneumoniae (Kp-MH258) as a target function, we engineered a functional 15-member synthetic microbiome--SynCom15--through a statistical approach agnostic to strain phenotype, mechanism of action, bacterial interactions, or composition of natural microbiomes. Our approach involved designing, building, and testing 96 metagenomically diverse consortia, learning a generative model using community strain presence/absence as input, and distilling model constraints through statistical inference. SynCom15 cleared Kp-MH258 across in vitro, ex vivo, and in vivo environments, matching the efficacy of a fecal microbiome transplant in a clinically relevant murine model of infection. The mechanism of suppression by SynCom15 was related to fatty acid production coupled with environmental acidification. SynCom15 also suppressed other pathogens--Clostridioides difficile, Escherichia coli, and other K. pneumoniae strains--but through different mechanisms. Sensitivity analysis revealed models trained on strain presence/absence captured the statistical structure of pathogen suppression, illustrating that community representation was key to our approach succeeding. Our framework, Constraint Distillation, could be a general and efficient strategy for building emergent complex systems, offering a path towards synthetic ecology more broadly.