Clostridioides difficile, a major cause of antibiotic associated diarrhea, is suppressed by the gut microbiome through multiple mechanisms; however, their relative importance in complex communities of the gut has not been fully described. Through meta-analysis of 12 human studies aided by machine learning approaches, we designed a synthetic fecal microbiota transplant (sFMT1) by reconstructing microbial networks negatively associated with C. difficile colonization in diverse human populations. This lab-built 37 strain consortia formed a functional community suppressing C. difficile in vitro and in animal models. Using sFMT1 as a tractable model system, we interrogated its function via a multi-omics approach and iterative reduction of community complexity. We demonstrated that both bile salt deconjugation and 7⍺-dehydroxylation of bile acids were not determinants of sFMT1 efficacy while a single strain which performs reductive Stickland fermentation of proline, a pathway of competitive nutrient utilization, was both necessary and sufficient to suppress C. difficile infection. This single strain replicated the efficacy of a healthy human fecal transplant in a gnotobiotic mouse model. Our data demonstrate the biological significance of bile acid metabolism in C. difficile resistance may be overestimated in complex communities while nutrient competition is a primary mechanism. sFMT1 is currently acting as a model system to further understand the maintenance and functional impact of lytic phages, and to decipher polymicrobial pathways of bile acid metabolism. These studies demonstrate a generalizable pipeline merging big data approaches with reductionist experimental models for the mechanistic interrogation of complex microbial communities.