Computational approaches to understanding Clostridioides difficile metabolism and virulence

Abstract

The progress of infection by Clostridioides difficile is strongly influenced by metabolic cues it encounters as it colonizes the gastrointestinal tract. Both colonization and regulation of virulence have a multi-factorial interaction between host, microbiome, and gene expression cascades. While these connections with metabolism have been understood for some time, many mechanisms of control have remained difficult to directly assay due to high metabolic variability among C. difficile isolates and difficult genetic systems. Computational systems offer a means to interrogate structure of complex or noisy datasets and generate useful, tractable hypotheses to be tested in the laboratory. Recently, in silico techniques have provided powerful insights into metabolic elements of C. difficile infection ranging from virulence regulation to interactions with the gut microbiota. In this review, we introduce and provide context to the methods of computational modeling that have been applied to C. difficile metabolism and virulence thus far. The techniques discussed here have laid the foundation for future multi-scale efforts aimed at understanding the complex interplay of metabolic activity between pathogen, host, and surrounding microbial community in the regulation of C. difficile pathogenesis.