Ion-pairing chromatography on a porous graphitic carbon column coupled with time-of-flight mass spectrometry for targeted and untargeted profiling of amino acid biomarkers involved in Candida albicans biofilm formation

Abstract

Background: Candida albicans, the most common fungal pathogen related to colonization and biofilm formation on the surfaces of indwelling medical devices, shows high resistance to the most commonly used antifungal drugs. In this study, an ion-pairing chromatography-porous graphitic carbon column coupled with a time-of-flight mass spectrometry (IP-PGC-TOF/MS) system was developed for targeted and untargeted profiling of metabolites involved in biofilm and planktonic growth of C. albicans. Using untargeted profiling analysis, 16 differential metabolites were screened and identified as potential biomarkers, most of which were amino acids or related compounds. Based on untargeted profiling analysis, targeted quantitative analysis of 22 amino acids was established and carefully evaluated using stable isotope-labeled internal standards. Among them, 9 amino acids that were not screened by untargeted profiling were further characterized as new biomarkers. Finally, a total of 25 potential biomarkers were screened using the combined targeted and untargeted strategy, among which 16 were characterized for the first time. Our results confirmed that amino acid metabolism and polyamine metabolism were at a high level in biofilms, except for some new biomarkers including ornithine, arginine and proline that were directly related to ornithine. Further experiments were carried out on the ornithine decarboxylase-negative (spe1Δ) mutant, and the results showed that the consumption of ornithine for putrescine biosynthesis has a significant impact on biofilm formation and may prove to be a drug target for resolving drug resistance of C. albicans. This study provides a systematic view of changes in amino acid metabolism during C. albicans biofilm formation by a combination of targeted and untargeted profiling using an original IP-PGC-TOF/MS method. It is a feasible approach for characterizing subtle variations and screening novel biomarkers from the microbial metabolome.