Clostridioides difficile, an antibiotic-resistant intestinal pathogen, is the leading cause of hospital-acquired infections in the United States. Treatment of a Clostridioides difficile, also known as C. difficile, infection may include courses of antibiotics or fecal transplants in which the fecal sample of a healthy donor is transplanted into a patient with C. difficile.
These treatments are not without their downsides. C. difficile cases treated with antibiotics frequently lead to recurrence of the infection, while issues with fecal transplants include a lack of standardization, possible transmission of pathogens and viruses like SARS-CoV-2, an inability to predict the effects of the treatment on the patient, and more.
Defined bacterial therapeutics may be a new avenue by which C. difficile infections can be treated once and for all. An R21 from the National Institutes of Health will help assistant professor Ophelia Venturelli and her co-investigator Daniel Amador-Noguez, an associate professor of bacteriology, tease apart the microbial interactions and metabolites impacting C. difficile growth. Freeman Lan, a postdoctoral scholar in the Venturelli Lab, has extensive experience developing ultrahigh-throughput techniques using droplet microfluidics and will lead this project.
“We want to understand the complex ecological and environmental factors that impact C. difficile growth in the gut microbiome to guide the design of defined bacterial therapeutics,” Venturelli said. “With this project we can start to get a sense of the multitude of different mechanisms that impact the growth of C. difficile.”
Venturelli’s lab will create millions of human gut microbiome communities in the lab building on a single-cell sequencing method developed by Lan. This will help them identify communities that enhance or inhibit the growth of C. difficile. Then, they will use machine learning and genome-scale metabolic modeling combined with exo-metabolomics, a subfield of metabolomics known as “metabolic footprinting” that studies metabolites outside of cells, to investigate the metabolite features and microbe-microbe interactions driving the growth of C. difficile.
“A detailed and mechanistic understanding of the diverse community types and metabolic properties that impact C. difficile growth will be a major advance towards designing safe and effective treatments for this intestinal pathogen,” said Venturelli. “More generally, the novel combination of ultrahigh-throughput ecological screening, machine learning and metabolomics could be used to understand microbial interactions influencing pathogens, beneficial bacteria or target metabolic activities.”