Candida albicans is a commensal fungus in healthy humans that causes infection in immunocompromised individuals through the secretion of several virulence factors. The successful establishment of infection is owing to elaborate strategies to cope with defensive molecules secreted by the host, including responses toward oxidative stress. Extracellular vesicle (EV) release is considered an alternative to the biomolecule secretory mechanism that favors fungal interactions with the host cells. During candidiasis establishment, the host environment becomes oxidative, and it impacts EV release and cargo. To simulate the host oxidative environment, we added menadione (an oxidative stress inducer) to the culture medium, and we explored C. albicans EV metabolites by metabolomics analysis. This study characterized lipidic molecules transported to an extracellular milieu by C. albicans after menadione exposure. Through Liquid Chromatography coupled with Mass Spectrometry (LC-MS) analyses, we identified biomolecules transported by EVs and supernatant. The identified molecules are related to several biological processes, such as glycerophospholipid and sphingolipid pathways, which may act at different levels by tuning compound production in accordance with cell requirements that favor a myriad of adaptive responses. Taken together, our results provide new insights into the role of EVs in fungal biology and host–pathogen interactions.

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Small molecules are components of fungal extracellular vesicles (EVs), but their biological roles are only superficially known. NOP16 is a eukaryotic gene that is required for the activity of benzimidazoles against Cryptococcus deuterogattii . In this study, during the phenotypic characterization of C. deuterogattii mutants lacking NOP16 expression, we observed that this gene was required for EV production.

GUIDE FOR CHROMATOGRAPHY COUPLED TO MASS SPECTROMETRY DATA PROCESSING. In this work, a discussed and step-wise tutorial for LC-MS and GC-MS data processing using the open-access software MZMine2 is presented and discussed. The rationale behind each step was demonstrated to enable the readers to go through their own data and process it accordingly. The main lesson to be learned is that each parameter must be chosen in light of the raw data and no guidelines should suggest a predetermined value. Still, it is worth mentioning that ideal values for each parameter do not exist, and that the user might end up investing too much time futilely optimizing values. Our suggestion is to process your data in light of the raw data (and the study design) following the preview figure result and the resulting feature list generated in each processing step, interpret your data, and go back to process it again to tune the detection of important features.