Functional profile of host microbiome indicates Clostridioides difficile infection

Area of Science:

• Microbiome research
• Infectious disease diagnostics
• Gastroenterology

Background:

• Clostridioides difficile infection (CDI) arises from disruptions in host-microbiome symbiosis, making the gut vulnerable to opportunistic pathogens.
• Fecal microbiota transplantation (FMT) shows promise for reversing these disruptions, but microbiome-based diagnostics for CDI are underdeveloped.

Purpose of the Study:

• To assess the diagnostic potential of the fecal microbiome for predicting CDI.
• To differentiate CDI impact from antibiotic therapy effects on the microbiome.
• To develop a quantitative score for CDI likelihood and evaluate FMT success.

Main Methods:

• Utilized metagenomic sequencing data from ten studies, including CDI patients (pre- and post-FMT), non-CDI patients, and healthy controls on antibiotics.
• Developed a hybrid species/function profiling approach to determine microbial community composition and function.
• Applied logistic regression models for classification and devised a log-odds score for CDI likelihood.

Main Results:

• The hybrid microbiome profiling approach demonstrated high accuracy in predicting CDI.
• Antibiotic therapy's confounding impact on CDI prediction was assessed and found to be distinguishable.
• The log-odds score effectively quantified CDI likelihood and showed microbiome improvement post-successful FMT.

Conclusions:

• The species and functional composition of the gut microbiome robustly predicts CDI.
• Microbiome analysis can distinguish CDI from antibiotic effects.
• A quantitative microbiome-based score can monitor CDI status and assess FMT efficacy, indicating restoration of gut function.