Genome analysis and machine learning-based feature selection strategy reveal potential drug-resistance determinants in <i>Nakaseomyces glabratus</i>

Invasive candidiasis caused by <i>Nakaseomyces glabratus</i> is of great concern due to high morbidity and mortality, especially antifungal resistance. To identify genomic signatures, which significantly link to drug-resistance, is of great significance in combating this lethal disease. In this study, we performed whole genome analysis on 109 clinical strains of <i>N. glabratus</i> which had been isolated from multi-centres in China. By using genome-wide association studies (GWAS), genomic signatures, including several <i>PDR1</i> mutations and genes encoding GLEYA-containing proteins, were identified to be significantly linked to drug-resistance. With the strategy of feature-selection combining machine-learning (ML), more relevant genomic signatures and potential resistance determinants were identified, including Y682C and I380L mutations in <i>PDR1</i> which were further confirmed to confer triazole-resistance by gene editing technology. We believe that the ML-based feature selection (MLFS) strategy, which is based on a comprehensive understanding of genomic characteristics as described in this study, shows excellent capacity to predict resistance and potential resistance determinants in <i>N. glabratus</i>.