The widespread use of antibiotics in our healthcare system has led to the emergence and dissemination of pathogens that have evolved to thrive in antibiotic treated hosts. A collateral impact of antibiotic treatment is the disruption of the microbial communities associated with healthy hosts. Successful pathogens have therefore evolved to navigate this altered competitive landscape to colonize and infect antibiotic treated patients. More effective prevention and treatment of healthcare associated infections will require a more nuanced understanding of how antibiotic treatment and host status jointly impact host microbial communities, and in turn, how pathogens exploit these disrupted communities to cause disease. Here we will focus on one of the most deadly healthcare associated pathogens, Clostridium difficile, for which antibiotic treatment and disruption of host microbiota have been shown to be major risk factors. To determine how host status, treatment regimens and disruption of the microbiota jointly contribute to C. difficile colonization and infection we will utilize existing, large patient cohorts, for which we will develop and validate computational models that integrate patient medical records and molecular readouts of microbial community structure and function. Hypotheses arising from these human subject studies will then be evaluated in our existing mouse models, where detailed characterization of the molecular interaction networks underlying colonization and infection will be determined. The proposed work will comprise two specific aims. In the first aim, risk factors for the development of complicated C. difficile infection (severe or recurrent disease) will be defined and incorporated into predictive models. This work is designed to provide actionable data that clinicians can use to guide therapy and potentially preventative measures. In the second aim, we will extend this analysis to risk factors that are associated with acquisition (colonization) by C. difficile. This work is aimed at guiding methods for infection prevention and control. Thus, this proposal will yield both immediately actionable insights into more effective treatment and prevention of C. difficile infections and detailed characterization of model systems wherein future therapeutics can be designed and evaluated.
The antibiotic era is currently in jeopardy, as widespread use and misuse of antibiotics has led to the spread of pathogens such as C. difficile that are recalcitrant to standard treatments. Here, we plan to lay the groundwork for both short and long-term solutions to this crisis. We will first characterizing factors that put patients at risk for te acquisition and disease due to C. difficile. We will also use animal models to gain insight to how susceptible patients can be protected and cured.
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|Shimasaki, Teppei; Seekatz, Anna; Bassis, Christine et al. (2018) Increased relative abundance of Klebsiella pneumoniae carbapenemase-producing Klebsiella pneumoniae within the gut microbiota is associated with risk of bloodstream infection in long-term acute care hospital patients. Clin Infect Dis :|
|Seekatz, Anna M; Wolfrum, Emily; DeWald, Christopher M et al. (2018) Presence of multiple Clostridium difficile strains at primary infection is associated with development of recurrent disease. Anaerobe :|
|Rao, Krishna; Higgins, Peter D R; Young, Vincent B (2018) An Observational Cohort Study of Clostridium difficile Ribotype 027 and Recurrent Infection. mSphere 3:|
|Jenior, Matthew L; Leslie, Jhansi L; Young, Vincent B et al. (2017) Clostridium difficile Colonizes Alternative Nutrient Niches during Infection across Distinct Murine Gut Microbiomes. mSystems 2:|
|Ulrich, Robert J; Santhosh, Kavitha; Mogle, Jill A et al. (2017) Is Clostridium difficile infection a risk factor for subsequent bloodstream infection? Anaerobe 48:27-33|
|Young, Vincent B (2017) Treatment With Fecal Microbiota Transplantation: The Need for Complete Methodological Reporting for Clinical Trials. Ann Intern Med 167:61-62|
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