Untargeted Metabolomic and Lipidomic Profiling in Cystic Fibrosis Patients Using UPLC-QTOF-MS.

Area of Science:

• Biochemistry
• Genetics
• Metabolomics

Background:

• Cystic fibrosis (CF) is a rare, autosomal recessive genetic disorder caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene.
• CF leads to impaired chloride ion transport, resulting in chronic respiratory infections, inflammation, and progressive lung function decline.
• Current diagnostic and monitoring methods for CF can be improved with the identification of reliable biomarkers.

Purpose of the Study:

• To identify plasma biomarkers for early detection, diagnosis, and monitoring of cystic fibrosis.
• To investigate metabolic and lipidomic profiles differentiating CF patients from healthy individuals.
• To uncover key metabolic pathways affected in cystic fibrosis.

Main Methods:

• Untargeted metabolomic and lipidomic analyses were performed on plasma samples.
• Liquid chromatography coupled with time-of-flight mass spectrometry (LC-TOF-MS) was used for sample analysis.
• Multivariate statistical and pathway enrichment analyses were applied to identify significant differences and affected pathways.

Main Results:

• Significant dysregulation was observed in multiple metabolic pathways, including galactose metabolism, glycolysis/gluconeogenesis, bile acid metabolism, fatty acid metabolism, steroid hormone biosynthesis, and amino acid catabolism.
• Specific plasma biomarkers were identified through combined lipidomic and metabolomic analyses.
• The study successfully discriminated between cystic fibrosis patients and healthy controls.

Conclusions:

• Metabolomic and lipidomic profiling can identify novel plasma biomarkers for cystic fibrosis.
• These identified biomarkers hold potential for improving early diagnosis and disease management in CF patients.
• Understanding metabolic pathway dysregulation provides insights into CF pathophysiology.