Raman Metabolomics of Candida auris Clades: Profiling and Barcode Identification.

This study targets on-site/real-time taxonomic identification and metabolic profiling of seven different Candida auris clades/subclades by means of Raman spectroscopy and imaging. Representative Raman spectra from different Candida auris samples were systematically deconvoluted by means of a customized machine-learning algorithm linked to a Raman database in order to decode structural differences at the molecular scale. Raman analyses of metabolites revealed clear differences in cell walls and membrane structure among clades/subclades. Such differences are key in maintaining the integrity and physical strength of the cell walls in the dynamic response to external stress and drugs. It was found that Candida cells use the glucan structure of the extracellular matrix, the degree of α-chitin crystallinity, and the concentration of hydrogen bonds between its antiparallel chains to tailor cell walls' flexibility. Besides being an effective ploy in survivorship by providing stiff shields in the α-1,3-glucan polymorph, the α-1,3-glycosidic linkages are also water-insoluble, thus forming a rigid and hydrophobic scaffold surrounded by a matrix of pliable and hydrated ß-glucans. Raman analysis revealed a variety of strategies by different clades to balance stiffness, hydrophobicity, and impermeability in their cell walls. The selected strategies lead to differences in resistance toward specific environmental stresses of cationic/osmotic, oxidative, and nitrosative origins. A statistical validation based on principal component analysis was found only partially capable of distinguishing among Raman spectra of clades and subclades. Raman barcoding based on an algorithm converting spectrally deconvoluted Raman sub-bands into barcodes allowed for circumventing any speciation deficiency. Empowered by barcoding bioinformatics, Raman analyses, which are fast and require no sample preparation, allow on-site speciation and real-time selection of appropriate treatments.

Raman Study of Pathogenic Candida auris: Imaging Metabolic Machineries in Reaction to Antifungal Drugs.

The multidrug-resistant Candida auris often defies treatments and presently represents a worldwide public health threat. Currently, the ergosterol-targeting Amphotericin B (AmB) and the DNA/RNA-synthesis inhibitor 5-flucytosine (5-FC) are the two main drugs available for first-line defense against life-threatening Candida auris infections. However, important aspects of their mechanisms of action require further clarification, especially regarding metabolic reactions of yeast cells. Here, we applied Raman spectroscopy empowered with specifically tailored machine-learning algorithms to monitor and to image in situ the susceptibility of two Candida auris clades to different antifungal drugs (LSEM 0643 or JCM15448T, belonging to the East Asian Clade II; and, LSEM 3673 belonging to the South African Clade III). Raman characterizations provided new details on the mechanisms of action against Candida auris Clades II and III, while also unfolding differences in their metabolic reactions to different drugs. AmB treatment induced biofilm formation in both clades, but the formed biofilms showed different structures: a dense and continuous biofilm structure in Clade II, and an extra-cellular matrix with a "fluffy" and discontinuous structure in Clade III. Treatment with 5-FC caused no biofilm formation but yeast-to-hyphal or pseudo-hyphal morphogenesis in both clades. Clade III showed a superior capacity in reducing membrane permeability to the drug through chemically tailoring chitin structure with a high degree of acetylation and fatty acids networks with significantly elongated chains. This study shows the suitability of the in situ Raman method in characterizing susceptibility and stress response of different C. auris clades to antifungal drugs, thus opening a path to identifying novel clinical solutions counteracting the spread of these alarming pathogens.

Raman Spectroscopy of Oral Candida Species: Molecular-Scale Analyses, Chemometrics, and Barcode Identification.

Oral candidiasis, a common opportunistic infection of the oral cavity, is mainly caused by the following four Candida species (in decreasing incidence rate): Candida albicans, Candida glabrata, Candida tropicalis, and Candida krusei. This study offers in-depth Raman spectroscopy analyses of these species and proposes procedures for an accurate and rapid identification of oral yeast species. We first obtained average spectra for different Candida species and systematically analyzed them in order to decode structural differences among species at the molecular scale. Then, we searched for a statistical validation through a chemometric method based on principal component analysis (PCA). This method was found only partially capable to mechanistically distinguish among Candida species. We thus proposed a new Raman barcoding approach based on an algorithm that converts spectrally deconvoluted Raman sub-bands into barcodes. Barcode-assisted Raman analyses could enable on-site identification in nearly real-time, thus implementing preventive oral control, enabling prompt selection of the most effective drug, and increasing the probability to interrupt disease transmission.

Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification.

Invasive fungal infections caused by yeasts of the genus Candida carry high morbidity and cause systemic infections with high mortality rate in both immunocompetent and immunosuppressed patients. Resistance rates against antifungal drugs vary among Candida species, the most concerning specie being Candida auris, which exhibits resistance to all major classes of available antifungal drugs. The presently available identification methods for Candida species face a severe trade-off between testing speed and accuracy. Here, we propose and validate a machine-learning approach adapted to Raman spectroscopy as a rapid, precise, and labor-efficient method of clinical microbiology for C. auris identification and drug efficacy assessments. This paper demonstrates that the combination of Raman spectroscopy and machine learning analyses can provide an insightful and flexible mycology diagnostic tool, easily applicable on-site in the clinical environment.