AUTHOR=Pezzotti Giuseppe , Kobara Miyuki , Nakaya Tamaki , Imamura Hayata , Asai Tenma , Miyamoto Nao , Adachi Tetsuya , Yamamoto Toshiro , Kanamura Narisato , Ohgitani Eriko , Marin Elia , Zhu Wenliang , Nishimura Ichiro , Mazda Osam , Nakata Tetsuo , Makimura Koichi 

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

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.896359

DOI=10.3389/fmicb.2022.896359

ISSN=1664-302X

ABSTRACT=Systemic fungal infections are one of the major causes of mortality in immunocompromised individuals. Among fungal pathogens, the multidrug-resistant Candida auris represents a worldwide public health threat. Its fungal infections very often defy treatments be-cause most clinical clades resist to one or more classes of antifungal drugs. Because of drugs toxicity to the host, the lowest possible ef-fective doses need to be given at the earliest stage of infection. Cur-rently, the ergosterol-targeting Amphotericin B and the DNA/RNA-synthesis inhibitor 5-flucytosine are the two main drugs available for first-line defense against life-threatening Candida auris infec-tions. However, important aspects of their mechanisms of action require further clarification, especially regarding metabolic reac-tions of yeast cells. Here, we apply Raman spectroscopy empowered with specifically tailored machine-learning algorithms to monitor and to image in situ the susceptibility of two Candida auris clades to antifungal drugs. Raman characterizations provided new details on the mechanisms of action against Candida auris Clades II and III, while also unfolding the different metabolic reactions of these two clades to different drugs.