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The gut microbiome is believed to play a role in the susceptibility to and treatment of Clostridium difficileinfections (CDIs). It is, however, unknown whether the gut microbiome is also affected by asymptomatic C difficilecolonization. Our study aimed to evaluate the fecal microbiome of children based on C difficilecolonization, and CDI risk factors, including antibiotic use and comorbid inflammatory bowel disease (IBD). Subjects with IBD and non-IBD controls were prospectively enrolled from pediatric clinics for a biobanking project (n = 113). A fecal sample was collected from each subject for research purposes only and was evaluated for asymptomatic toxigenic C difficilecolonization. Fecal microbiome composition was determined by 16S rRNA sequencing. We found reduced bacterial diversity and altered microbiome composition in subjects with C difficilecolonization, concurrent antibiotic use, and/or concomitant IBD (all P< 0.05). Accounting for antibiotic use and IBD status, children colonized with C difficilehad significant enrichment in taxa from the genera Ruminococcus, Eggerthella, and Clostridium. Children without C difficilehad increased relative abundances of Faecalibacteriumand Rikenellaceae. Imputed metagenomic functions of those colonized were enriched for genes in oxidative phosphorylation and beta-lactam resistance, whereas in the subjects without C difficile, several functions in translation and metabolism were over-represented. In children, C difficilecolonization, or factors that predispose to colonization such as antibiotic use and IBD status were associated with decreased gut bacterial diversity and altered microbiome composition. Averting such microbiome alterations may be a method to prevent or treat CDI.

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.