Project Summary In their 2019 Antibiotic Resistance Threats Report, the Centers for Disease Control and Prevention listed Clostridioides (formerly Clostridium) difficile as an urgent threat. As the most common healthcare-associated infection, it has an enormous impact on both the lives of individuals and the healthcare system at large. Developing a C. difficile infection (CDI) is most often associated with the recent use of antibiotics, as broad spectrum antibiotics can lead to a disruption of the normal gut microbiota, which in turn allows C. difficile spores to germinate and overwhelm the remaining microbiome that normally keeps the vegetative C. difficile at bay. Although the patient risk factors for CDI are fairly well understood, the potential roles of genetic variation in the infecting strain in influencing the progression to severe CDI are less so. Given the extensive diversity in both the nucleotide sequences of core genes and variation in gene content among common C. difficile strains, it is likely that there are significant differences in how different strains of C. difficile interact with the host. Indeed, there have been numerous reports of variation in the propensity for certain sequence types to cause severe disease, although the genetic variation mediating strain-level differences is largely unknown. In this proposal I take a data driven approach to identify genetic variants influencing patient immune responses and clinical trajectories. To accomplish this, I will leverage a massive data repository created through comprehensive sampling of all C. difficile positive cases at Michigan Medicine. Included in this repository are 1,678 C. difficile whole genome sequenced isolates, associated processed electronic health record data from 1,516 patients, and banked serum during the instance of CDI for 1178 patients. Serum cytokine levels have already been determined for 220 of these patients. Preliminary studies conducted in support of this proposal demonstrate that variation encoded in the genomes of infecting strains are predictive of both initial patient immune responses and subsequent severe infections, supporting the contribution of strain genetic background to patient clinical trajectories. I will build upon these studies and attempt to identify the specific variants, genes and pathways that are mediating variation in clinical outcomes. To this end I will employ a combination of machine learning and bacterial genome-wide association studies (bGWAS) to gain insight into bacterial genetic features that influence patient immune response as quantified by serum cytokine measurements, as well as bacterial genetic variation associated with severe outcomes. I will then validate these bioinformatic findings by evaluating the accuracy of model predictions by comparison of predicted and actual 1) cytokine measures on withheld serum samples, and 2) in vivo severity outcome in a mouse model of CDI. The resulting understanding of the genetic fact...