Multi-omics integration to model the differential metabolic activities of Candida glabrata and bacteria in humans with C. difficile infection - Abstract
C. difficile infection (CDI) is the most common nosocomial bacterial infection in the United States, accounting
for more than 1.5 billion dollars in annual healthcare expenditures in this country. The most common and
resource intensive treatment gap in CDI is disease persistence and recurrence, which together occurs in 15-
30% of patients following a first course of antibiotic therapy. Recent evidence suggests a role for cooperative
transkingdom interactions between fungi and bacteria in this disease. Dr. Lamendella has produced most of
these data in microbiome studies from mice and humans. Mice pre-colonized with Candida glabrata
experienced greater severity and lethality of CDI. Dr. Lamendella has led three different, published human
studies which revealed strong enrichment of fungal taxa, especially C. glabrata, among CDI patients, while
patients of comparable age, gender, chronic comorbidities, and antibiotic exposure with C. difficile negative
diarrhea lack this fungal enrichment. With comparable antibiotic exposure between our cohorts, this fungal
enrichment is not an antibiotic epiphenomenon, but rather it represents a previously unrecognized component
of CDI that influences disease severity while serving as a promising, previously unappreciated therapeutic
target. The central hypothesis of this work is that C. glabrata and C. difficile have interspecies
interactions through metabolic and signaling pathways that affect CDI severity and outcome. We will
use CDI+ and CDI- human stool to identify key metabolic pathways and interactions between C. glabrata and
C. difficile to further describe the role of fungi in CDI pathogenesis. We will perform matched metagenomic
(MG) and metatranscriptomic (MT) studies of >500 human stools linked with deidentified clinical and
immunological metadata, obtained from three collaborating university hospitals to identify how C. glabrata
influences CDI through mechanisms including nutrient competition, modulation of the immune response,
quorum sensing, and biofilm formation. Dr. Lamendella will lead a team of student researchers, with a
particular emphasis on recruiting underrepresented students, who will leverage high-thoughput sequencing,
bioinformatics analyses, and machine learning models to discover the functional underpinnings of fungi’s role
in CDI. Our first aim will validate our preliminary observations that fungi (especially C. glabrata) directly or
indirectly exacerbate CDI. This aim will establish the prevalence and differential activity of C. glabrata in CDI.
These data are integral to determining the differential metabolic pathways and will enable multivariate
associations with CDI status, clinical metadata, immune markers, and microbial meta-omics features. The
second aim of this proposal will validate features of C. glabrata that are predictive of CDI using genome-scale
metabolic models. Machine learning will be used to extract relevant features that best distinguish CDI. The
long-term goal is to further develop the novel concept, first described by Dr. Lamendella’s team, that fungi have
a role in CDI and are a promising therapeutic target for this disease.
