WS04.02: Gut microbiota-metabolome relationships across CFTR modulator usage: preliminary findings from the GRAMPUS-CF study

Objectives
Despite shifts to highly effective CFTR modulators, some people with cystic fibrosis (pwCF) continue to experience intestinal abnormalities, associated symptoms, and gut microbiota dysbiosis. Baseline faecal samples across children and adults in the Gut Research Advancing a Mechanistic and Personalised Understanding of Symptoms in Cystic Fibrosis (GRAMPUS-CF) study were used to investigate relationships between gut microbiota composition and the intestinal metabolome under elexacaftor/tezacaftor/ivacaftor (ETI) treatment.
Methods
Gut microbiota profile was determined using PacBio full-length 16S HiFi sequencing (n=70). Untargeted metabolomics, including additional lipidomics, were performed with ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) (n=64). Correlation-based approaches were used to construct networks highlighting relationships between taxa and metabolites across paired samples. Participant clinical data was integrated to investigate associations with microbiota structure and function.
Results
A wide array of unique metabolites from the metabolomic (n=660) and lipidomic approaches (n=527) were identified from participant faecal samples. Network analyses highlighted both positive and negative associations across beneficial and potentially pathogenic taxa, extending to relationships with metabolites and lipids of physiological interest in the CF intestine. Clinical demographics also explained the variance observed across microbiota structure, alongside metabolomic and lipidomic profiles.
Conclusions
Faecal multi-omics indicate important relationships with gut microbiota structure across pwCF undertaking CFTR modulator therapy. Temporal dynamics of the gut microbiome across these participants will be investigated across ETI therapy in the GRAMPUS-CF study. Further clinical data, participant symptomatic data, and intestinal physiology metrics will support our future analyses.