Antifungal resistance is the leading cause of antifungal treatment failure in invasive candidiasis. Metabolic rewiring could become a new insight to account for antifungal resistance as to find innovative clinical therapies. Here, we show that dynamic surface-enhanced Raman spectroscopy is a promising tool to identify the metabolic differences between fluconazole-resistant and fluconazole-sensitive Candida albicans through the signatures of biochemical components and complemented with machine learning algorithms and two-dimensional correlation spectroscopy, an underlying resistance mechanism, that is, the change of purine metabolites induced the resistance of Candida albicans has been clarified yet never reported anywhere. We hope the integrated methodology introduced in this work could be beneficial for the interpretation of cellular regulation, propelling the development of targeted antifungal therapies and diagnostic tools for more efficient management of severe antifungal resistance.
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