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Finding Candida auris in public metagenomic repositories
Jorge E Mario-Vasquez1, Ujwal R Bagal2, Elijah Lowe3
1Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Abstract:
Candida auris is a newly emerged multidrug-resistant fungus capable of causing invasive infections with high mortality. Despite intense efforts to understand how this pathogen rapidly emerged and spread worldwide, its environmental reservoirs are poorly understood. Here, we present a collaborative effort between the U.S. Centers for Disease Control and Prevention, the National Center for Biotechnology Information, and GridRepublic (a volunteer computing platform) to identify C. auris sequences in publicly available metagenomic datasets. We developed the MetaNISH pipeline that uses SRPRISM to align sequences to a set of reference genomes and computes a score for each reference genome. We used MetaNISH to scan ~300,000 SRA metagenomic runs from 2010 onwards and identified five datasets containing C. auris reads. Finally, GridRepublic has implemented a prospective C. auris molecular monitoring system using MetaNISH and volunteer computing.
Insights
Researchers identified Candida auris (a multidrug-resistant fungus) in environmental samples using a new pipeline. This discovery aids in understanding the spread of this dangerous pathogen.
Area of Science:
- Medical Mycology
- Computational Biology
- Environmental Microbiology
Background:
- Candida auris is an emerging multidrug-resistant fungus causing invasive infections with high mortality.
- Understanding the environmental reservoirs of Candida auris is crucial for controlling its global spread.
Purpose of the Study:
- To identify Candida auris sequences within publicly available metagenomic datasets.
- To develop and implement a system for prospective molecular monitoring of Candida auris.
Main Methods:
- Development of the MetaNISH pipeline utilizing SRPRISM for sequence alignment against reference genomes.
- Scanning approximately 300,000 Sequence Read Archive (SRA) metagenomic runs from 2010 onwards.
- Leveraging volunteer computing via GridRepublic for large-scale data analysis.
Main Results:
- Identification of five metagenomic datasets containing Candida auris reads.
- Successful implementation of a prospective molecular monitoring system for Candida auris.
Conclusions:
- The MetaNISH pipeline effectively identifies Candida auris in metagenomic data.
- The developed monitoring system provides a framework for ongoing surveillance of this pathogen in environmental reservoirs.
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