AsianScientist (Aug. 28, 2024) – Researchers in Singapore have identified a new clade of Candida auris, a highly resistant and easily transmissible fungus. This discovery adds a sixth clade to the global list. Candida auris is a major global health concern due to its high transmissibility, resistance to multiple treatments and tendency to cause outbreaks. This discovery, made by researchers from Singapore General Hospital, A*STAR’s Genome Institute of Singapore (GIS), and the Yong Loo Lin School of Medicine at the National University of Singapore (NUS Medicine), was published in The Lancet Microbe.
Candida auris, or C. auris, is hard to eradicate. It mostly affects patients with severe underlying medical conditions. Those with invasive medical devices like breathing or feeding tubes and catheters tend to be at higher risk of getting this fungus and developing a range of infections from superficial to life-threatening ones.
In 2022, the World Health Organization listed C. auris as a critical priority for research and public health action. The US Centers for Disease Control and Prevention has also declared the fungus as an ‘urgent antimicrobial resistance threat’ as it has become increasingly difficult to treat, further emphasizing the need to understand and mitigate this public health threat.
“Now that we have discovered the sixth Candida auris clade, there is a pressing need to improve surveillance capability or develop new methods to augment current surveillance strategies so that healthcare facilities can keep a close watch on its emergence and contain the spread once found,” said Karrie Ko, co-first author of the study. She is a consultant at the Department of Microbiology, Singapore General Hospital, and genomics director of the Pathology Academic Clinical Programme under SingHealth and Duke-NUS Medical School.
So far, five geographically distinct C auris clades have been identified – clade I (south Asian), clade II (east Asian), clade III (African), clade IV (South American), and clade V (Iranian). The clade VI isolates found in Singapore have not yet been linked to any specific geographic region.
The Singapore General Hospital runs an active surveillance programme which screens high-risk patients for C. auris through a routine swab upon admission. Patients who are tested positive are immediately isolated and all inpatients who share the same ward or room are screened to contain its spread.
The new clade was detected in 2023 after a patient in the hospital tested positive for C. auris, which is commonly associated with overseas travel. The patient, however, had remained in Singapore for two years prior, which prompted further investigations.
Scientists from Genome Institute of Singapore developed a machine-learning technique to keep track of a potential new C. auris clade. They then reconstructed the C. auris genomes and performed an in-depth characterisation of the genomes to confirm the emergence of a new clade. Upon discovering that the patient had C. auris belonging to a clade that was genetically different from the other five, the team looked through the hospital’s archive and found two other patient cases.
“Genomic surveillance is essential for understanding emerging pathogens. By integrating genomics, metagenomics and collaborative efforts among researchers and clinicians, we can continually enhance our pandemic preparedness and response to public health threats,” said senior author of the study, associate professor Niranjan Nagarajan, from the Infectious Diseases Translational Research Programme at NUS Medicine, and associate director, Genome Architecture, and Senior Group Leader, Laboratory of Metagenomic Technologies and Microbial Systems at A*STAR’s GIS.
The researchers are now working on a proof-of-concept machine learning approach that can automatically detect new clades early. This is particularly important for regional hubs like Singapore, which receives a high number of international visitors, to monitor and identify emerging public health threats early, the study said.
“This study demonstrated that machine learning approach can improve surveillance capabilities by automatically detecting unusual outlier genomes. Our human-in-the-loop machine learning workflow facilitates continuous learning from new data, so that we can detect and investigate potential novel genomes as early as possible. This has the potential to strengthen surveillance against emerging public health threats,” said co-first author of the study, Chayaporn Suphavilai, senior scientist at A*STAR’s GIS.
The research stated that since the source, risk factors, geographic links, virulence, and potential for outbreaks of clade VI C. auris remain unknown, it is imperative to prioritize early detection and containment to ensure patient safety.
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Source: Singapore General Hospital ; Image: Shutterstock
The article can be found at Detection and characterisation of a sixth Candida auris clade in Singapore: a genomic and phenotypic study
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