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Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance
Published on: December 28, 2017
Antifungal heteroresistance causes prophylaxis failure and facilitates breakthrough Candida parapsilosis infections
Bing Zhai1,2,3, Chen Liao4, Siddharth Jaggavarapu5,6,7,8
1Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. bing.zhai@siat.ac.cn.
Abstract:
Breakthrough fungal infections in patients on antimicrobial prophylaxis during allogeneic hematopoietic cell transplantation (allo-HCT) represent a major and often unexplained cause of morbidity and mortality. Candida parapsilosis is a common cause of invasive candidiasis and has been classified as a high-priority fungal pathogen by the World Health Organization. In high-risk allo-HCT recipients on micafungin prophylaxis, we show that heteroresistance (the presence of a phenotypically unstable, low-frequency subpopulation of resistant cells (~1 in 10,000)) underlies breakthrough bloodstream infections by C. parapsilosis. By analyzing 219 clinical isolates from North America, Europe and Asia, we demonstrate widespread micafungin heteroresistance in C. parapsilosis. Standard antimicrobial susceptibility tests, such as broth microdilution or gradient diffusion assays, which guide drug selection for invasive infections, fail to detect micafungin heteroresistance in C. parapsilosis. To facilitate rapid detection of micafungin heteroresistance in C. parapsilosis, we constructed a predictive machine learning framework that classifies isolates as heteroresistant or susceptible using a maximum of ten genomic features. These results connect heteroresistance to unexplained antifungal prophylaxis failure in allo-HCT recipients and demonstrate a proof-of-principle diagnostic approach with the potential to guide clinical decisions and improve patient care.
Insights
Breakthrough fungal infections in patients undergoing allogeneic hematopoietic cell transplantation (allo-HCT) are often caused by Candida parapsilosis heteroresistance to micafungin. Standard tests miss this resistance, impacting treatment effectiveness.
Area of Science:
- Mycology
- Infectious Diseases
- Hematology
Background:
- Breakthrough fungal infections, particularly invasive candidiasis, pose significant risks in patients receiving antimicrobial prophylaxis during allogeneic hematopoietic cell transplantation (allo-HCT).
- Candida parapsilosis, a WHO high-priority pathogen, is a frequent cause of invasive candidiasis, and its resistance patterns are critical for patient outcomes.
- Unexplained antifungal prophylaxis failures in high-risk allo-HCT recipients necessitate investigation into underlying resistance mechanisms.
Purpose of the Study:
- To investigate the role of micafungin heteroresistance in breakthrough Candida parapsilosis bloodstream infections in patients undergoing allo-HCT.
- To assess the prevalence of micafungin heteroresistance in C. parapsilosis isolates globally.
- To develop a novel diagnostic approach for detecting micafungin heteroresistance.
Main Methods:
- Analysis of 219 clinical C. parapsilosis isolates from diverse geographical regions.
- Evaluation of standard antimicrobial susceptibility testing methods for their ability to detect micafungin heteroresistance.
- Development and validation of a machine learning framework utilizing genomic features for heteroresistance prediction.
Main Results:
- Micafungin heteroresistance, characterized by a low-frequency resistant subpopulation, was identified as a key factor in breakthrough C. parapsilosis infections in allo-HCT patients.
- Widespread prevalence of micafungin heteroresistance was observed across global isolates of C. parapsilosis.
- Standard susceptibility assays failed to detect micafungin heteroresistance, highlighting a critical diagnostic gap.
Conclusions:
- Heteroresistance in Candida parapsilosis explains previously unexplained micafungin prophylaxis failures in allo-HCT recipients.
- A machine learning framework offers a promising tool for rapid and accurate detection of micafungin heteroresistance.
- This diagnostic approach has the potential to guide clinical decision-making and improve patient care in high-risk populations.
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