The primary risk factor for infection with members of the Klebsiella pneumoniae species complex is prior gut colonization, and infection is often caused by the colonizing strain. Despite the importance of the gut as a reservoir for infectious Klebsiella , little is known about the association between the gut microbiome and infection. To explore this relationship, we undertook a case-control study comparing the gut community structure of Klebsiella -colonized intensive care and hematology/oncology patients. Cases were Klebsiella -colonized patients infected by their colonizing strain (N = 83). Controls were Klebsiella -colonized patients that remained asymptomatic (N = 149). First, we characterized the gut community structure of Klebsiella -colonized patients agnostic to case status. Next, we determined that gut community data is useful for classifying cases and controls using machine learning models and that the gut community structure differed between cases and controls. Klebsiella relative abundance, a known risk factor for infection, had the greatest feature importance but other gut microbes were also informative. Finally, we show that integration of gut community structure with bacterial genotype or clinical variable data enhanced the ability of machine learning models to discriminate cases and controls. This study demonstrates that including gut community data with patient- and Klebsiella -derived biomarkers improves our ability to predict infection in Klebsiella -colonized patients.
Importance: Colonization is generally the first step in pathogenesis for bacteria with pathogenic potential. This step provides a unique window for intervention since a given potential pathogen has yet to cause damage to its host. Moreover, intervention during the colonization stage may help alleviate the burden of therapy failure as antimicrobial resistance rises. Yet, to understand the therapeutic potential of interventions that target colonization, we must first understand the biology of colonization and if biomarkers at the colonization stage can be used to stratify infection risk. The bacterial genus Klebsiella includes many species with varying degrees of pathogenic potential. Members of the K. pneumoniae species complex have the highest pathogenic potential. Patients colonized in their gut by these bacteria are at higher risk of subsequent infection with their colonizing strain. However, we do not understand if other members of the gut microbiota can be used as a biomarker to predict infection risk. In this study, we show that the gut microbiota differs between colonized patients that develop an infection versus those that do not. Additionally, we show that integrating gut microbiota data with patient and bacterial factors improves the ability to predict infections. As we continue to explore colonization as an intervention point to prevent infections in individuals colonized by potential pathogens, we must develop effective means for predicting and stratifying infection risk.