Fig 1.
Overview of metabolomic approaches and typical workflow.
The method of choice is shaped by the rationale of the experiment, the amount and type of the sample material (single or mixed cellular samples, tissues, supernatants/fluids, etc.), the decision for single measurements versus time course analyses, and whether the focus is on known or unknown (global) metabolic pathways/cellular processes (targeted versus untargeted approach, respectively). NMR is frequently used for the detection of biomarkers. Although this method provides high reproducibility and the option of in vivo metabolomics, it is less sensitive compared to MS techniques. Commonly used MS-based methods are MRM and SRI, usually performed with tandem MS. Typically, prior to MS measurements, a separation of the extracted metabolites is needed to increase sensitivity and specificity. Sample separation is done by either GC, more favorable for volatile compounds, or LC. HILIC is an LC technique optimized for separation of hydrophilic polar compounds, like carbohydrates. MALDI is a surface-based MS approach used in single-cell metabolomics, and for the determination of the spatial distribution of metabolites within a specimen via MALDI imaging. However, MALDI-based approaches are limited to abundant metabolites. The raw data processing includes method-dependent quality controls, as well as normalization and identification steps. The absolute quantification of the measured metabolites is only possible in targeted approaches. Finally, the processed data can be compared to existing databases, used for modeling of metabolic fluxes, or integrated with other OMICs datasets. GC, gas chromatography; HILIC, hydrophilic interaction chromatography; LC, liquid chromatography; MALDI, matrix-associated laser desorption ionization; MRM, multiple reaction monitoring; MS, mass spectrometry; NMR, nuclear magnetic resonance; SRI, selective reagent ionization.
Fig 2.
Metabolomics approaches in human pathogenic fungi.
An overview of the metabolomics approaches utilized to date in human pathogenic fungi, with a primary focus on Aspergillus, Candida, Cryptococcus, and Mucor spp. In Aspergillus, metabolomic studies were used to identify secondary metabolites and biomarkers as a means to improve earlier detection and diagnosis of aspergillosis [4, 5, 10, 11, 15–17, 40, 41]. To date, metabolomics approaches have not been utilized to investigate the primary metabolism of Aspergillus spp., which has been the main focus of C. albicans and C. neoformans research. Metabolomic approaches brought insight into C. albicans virulence mechanisms, antifungal effects, and biofilm growth [6, 13, 18, 21–25, 28–33, 35–39]. Although one study focused on secreted metabolites and biomarkers in C. auris and used metabolomics to study resistance mechanisms in this organism [14, 43], metabolomics approaches in other pathogenic Candida spp. are still lacking. Further, metabolomic-based ex vivo, in vivo, and secondary metabolites/biomarker identification in Candida spp. are either scarce or lacking. In Cryptococcus spp. metabolomics was utilized in the identification of biomarkers, resistance mechanisms to antifungal drugs, and in vivo infection models [12, 19, 20, 27, 34]. In Mucor spp., the only metabolomic approach undertaken has examined the metabolic effect of antifungal agents [26].