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Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance
Published on: December 28, 2017
Pan-omics-based characterization and prediction of highly multidrug-adapted strains from an outbreak fungal species
Xin Fan1,2,3, Lei Chen3, Min Chen4
1Department of Infectious Diseases and Clinical Microbiology, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China.
Abstract:
Strains from the Cryptococcus gattii species complex (CGSC) have caused the Pacific Northwest cryptococcosis outbreak, the largest cluster of life-threatening fungal infections in otherwise healthy human hosts known to date. In this study, we utilized a pan-phenome-based method to assess the fitness outcomes of CGSC strains under 31 stress conditions, providing a comprehensive overview of 2,821 phenotype-strain associations within this pathogenic clade. Phenotypic clustering analysis revealed a strong correlation between distinct types of stress phenotypes in a subset of CGSC strains, suggesting that shared determinants coordinate their adaptations to various stresses. Notably, a specific group of strains, including the outbreak isolates, exhibited a remarkable ability to adapt to all three of the most commonly used antifungal drugs for treating cryptococcosis (amphotericin B, 5-fluorocytosine, and fluconazole). By integrating pan-genomic and pan-transcriptomic analyses, we identified previously unrecognized genes that play crucial roles in conferring multidrug resistance in an outbreak strain with high multidrug adaptation. From these genes, we identified biomarkers that enable the accurate prediction of highly multidrug-adapted CGSC strains, achieving maximum accuracy and area under the curve (AUC) of 0.79 and 0.86, respectively, using machine learning algorithms. Overall, we developed a pan-omic approach to identify cryptococcal multidrug resistance determinants and predict highly multidrug-adapted CGSC strains that may pose significant clinical concern.
Insights
Cryptococcus gattii strains causing a major outbreak show remarkable multidrug resistance. Researchers identified genes and biomarkers to predict these dangerous fungal strains.
Area of Science:
- Medical Mycology
- Fungal Pathogenesis
- Genomics and Transcriptomics
Background:
- The Cryptococcus gattii species complex (CGSC) is responsible for a significant outbreak of cryptococcosis in the Pacific Northwest.
- This outbreak represents the largest known cluster of life-threatening fungal infections in immunocompetent individuals.
Purpose of the Study:
- To comprehensively assess the fitness of CGSC strains under diverse stress conditions.
- To identify genetic determinants of multidrug resistance in CGSC, particularly in outbreak strains.
- To develop predictive biomarkers for highly multidrug-adapted CGSC strains.
Main Methods:
- A pan-phenome-based approach was used to evaluate CGSC strain fitness across 31 stress conditions.
- Phenotypic clustering analysis was performed to identify correlations in stress adaptation.
- Integrated pan-genomic and pan-transcriptomic analyses were employed to discover resistance genes.
- Machine learning algorithms were utilized to develop predictive biomarkers.
Main Results:
- Over 2,800 phenotype-strain associations were identified.
- A subset of CGSC strains, including outbreak isolates, demonstrated adaptation to three key antifungal drugs.
- Previously unrecognized genes conferring multidrug resistance were identified in an outbreak strain.
- Biomarkers achieved high accuracy (0.79) and AUC (0.86) in predicting multidrug-adapted strains.
Conclusions:
- A pan-omic approach successfully identified determinants of multidrug resistance in Cryptococcus gattii.
- Predictive biomarkers can accurately identify clinically concerning, highly multidrug-adapted CGSC strains.
- This research provides critical insights into managing cryptococcosis outbreaks and antifungal resistance.
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