Predicting Adverse Outcomes in Clostridioides difficile Infections and Identifying Associated Host and Microbial Drivers of Disease Severity

Abstract

Clostridioides difficile is an anaerobic, toxigenic bacterium that causes nearly 500,000 gastrointestinal infections annually in the United States of America, with disease ranging from mild diarrhea to severe colitis and death. The goal of my thesis is to utilize a systems biology approach to examine C. difficile infection (CDI) pathogenesis in order to identify drivers of the variation seen in clinical outcome. The initial status of the system (host, microbiome, and pathogen) determines the trajectory of the infection, and fully exploring the variation seen in specific initial conditions allows for the discovery of emergent properties of the infection leading to adverse CDI outcomes.

CDI is a high concern for public health as it is rising in incidence, carries a high mortality and morbidity, and is difficult to treat, resulting in a multitude of emerging therapies currently being produced and tested in clinical trials for preventing and treating CDI. The wide variation of disease severity and high infection rate makes it imperative to identify patients that are at high risk of adverse outcomes early in the course of infection with C. difficile. We illustrate the ability of models utilizing serum inflammatory markers to predict which patients, at time of diagnosis, will develop adverse outcomes including ICU admission, colectomy and death. We also show that models produced on human data validate in experimentally infected mice with mild and severe CDI.

As there are high variations in patients in the clinic, we can use model systems to limit the variation we are exploring to identify what factors are associated with severe CDI outcomes. A majority of deaths from CDI occur in older individuals, indicating a relationship between age and the risk of developing severe CDI disease outcomes. We show that aging impacts the immune response to severe CDI in mice by altering serum inflammatory mediators and the mobilization of neutrophils and eosinophils.

The microbiota is a key player in the development of CDI and the progression of the disease. We utilize a systems biology approach to examine C. difficile infection pathogenesis to identify the host and microbial drivers of variation in clinical outcome. We assembled a cohort of mice that includes a limited degree of variation in age and sex. Using this cohort, we examined the microbial community types (“enterotypes”) that exist before and after antibiotics, during initial colonization with C. difficile, and at peak disease severity in mice. We describe the association of specific enterotypes with age, sex, colonization and subsequent disease severity.

The findings of this research highlight the complexity of C. difficile infections and the importance of a systems biology approach to its understanding. We find promising results for utilizing serum biomarker models for the determination of high-risk individuals at time of diagnosis, a potential method for allocating expensive and higher-risk emerging therapies to individuals who will develop adverse CDI outcomes. Our mouse cohort findings highlight the importance of age, sex, and microbial community structure in CDI pathogenesis and the prediction of adverse outcomes in CDI.