Background: Clostridioides difficile infection (CDI) is the most common cause of healthcare–associated infection and an important cause of morbidity and mortality among hospitalized patients. A comprehensive understanding of C. difficile infection (CDI) pathogenesis is crucial for disease diagnosis, treatment and prevention. To achieve that, a quantitative study of host-microbiome interactions in CDI is a prerequisite. Yet, an effective computational framework to quantify host-microbiome interactions in CDI was lacking.
Methods: Here, we characterized gut microbial compositions and abroad panel of immunological markers in a comprehensive clinical cohort of 243 well-characterized human subjects with four different C. difficile infection/colonization statuses (CDI, Asymptomatic Carriage, Non-CDI Diarrhea, and Control). Based on microbial and immunological features, we developed a computational framework to detect CDI status using random forest and symbolic classification models.
Results: First, by calculating the correlations between microbial compositions and the circulating levels of host immune markers for each of the four phenotype groups, we found that the interactions between gut microbiota and host immune markers are very sensitive to the status of C. difficile colonization and infection. Second, we demonstrated that incorporating both gut microbiome and host immune marker data into random forest classifiers can better distinguish CDI from other groups than can either type of data alone. Finally, we performed symbolic classification using selected features from random forest classifiers to derive simple mathematic formulas that explicitly model the interactions between gut microbiome and host immune markers.
Conclusions: Overall, this study provides an effective computational framework to quantify the role of the intricate interactions between gut microbiota and host immune markers in CDI pathogenesis. This framework may inform the design of future diagnostic and therapeutic strategies.