Candida auris is an emerging threat, primarily to hospital patients and residents of nursing homes. The fungus easily spreads, colonizes surfaces and objects where it can survive for weeks to months, is often resistant to standard disinfectants, and can cause life-threatening infections. Although those infections, in principle, can be treated with several antifungal medications, strains of the pathogen that have developed antimicrobial resistance (AMR) against those drugs have become a difficult challenge for hospital physicians.
“Clinicians need a much more effective diagnostic approach to accurately quantify the abundance of the pathogen in patients and assess its antifungal resistance in order to better respond to C. auris infections in their patients and help prevent future hospital-associated outbreaks,” said Justin Rolando, PhD, a postdoc in the Walt lab at the Wyss Institute. “Current diagnostic methods for detecting C. auris are too costly, slow, and dependent on complex equipment and trained personnel in order to effect real change.”
A new study presents a diagnostic approach that enables fast and accurate quantification of C. auris strains from swab samples, as well as the quantification of AMR-causing mutations in fungal populations with mixed antifungal susceptibility.
The findings are published in Nature Biomedical Engineering in the paper, “Digital CRISPR-based diagnostics for quantification of Candida auris and resistance mutations.”
The research team integrated SHERLOCK technology—a CRISPR-based diagnostic method that allows the detection of pathogen-derived (or other) nucleic acid sequences with single nucleotide precision with ultra-sensitive single-molecule microarray technology. By monitoring the development of finely tuned fluorescent signals produced by thousands of parallel single-molecule assays in real-time, and analyzing the signals using a machine learning-based artificial intelligence method, the team created a fast and quantitative approach, named dSHERLOCK (short for digital SHERLOCK), that measures the degree of fungal colonization of C. auris in patient samples and pinpoints the presence of mutations that cause specific antimicrobial resistances (AMRs).
Using dSHERLOCK, the researchers were able to reliably detect C. auris in swab samples, which they obtained from their collaborators at the Wadsworth Center. The assay displayed “excellent sensitivity to C. auris from major clades 1–4, while maintaining specificity when challenged with common environmental and pathogenic fungi,” the authors note. The assay is completed within 20 minutes and can accurately quantify how much of the pathogen the samples contained within 40 minutes.
Also, by tweaking the CRISPR-mediated detection mechanism, the researchers amplified C. auris targets that contained mutations associated with AMR and showed these two common antifungal drugs displayed different “kinetics.”
dSHERLOCK’S single-molecule detection assays are designed such that positive fluorescent signals produced from distinct targets are generated at different rates. This allows the identification of sequence-specific fluorescence signatures that corresponded to defined AMRs against the often-used azole and echinocandin antifungal drugs. The team was able to trace several of these signatures in an individual sample, which is key to optimizing treatments since existing diagnostics only pick up one strain of C. auris in an all-or-nothing fashion, preventing them from giving true guidance.
The team streamlined the assay’s reaction conditions to simplify its multistep process into a “one-pot-reaction” that proceeds autonomously from start to finish. But to realize dSHERLOCK’s full potential they needed to enhance its usefulness with a computational analytical pipeline.
“One of our microarrays contains about 18,000 individual compartments many of which contain a single C. auris target molecule—essentially the 1s in ‘digital’ SHERLOCK. Performing dSHERLOCK assays across all compartments provides us with an extraordinary large amount of fluorescent data that represent the pathogen’s presence, extent of fungal infection, as well as the pathogen’s genetic variability,” said Rolando.
“The capabilities that we are introducing with dSHERLOCK satisfy the major clinical requirements for a next-generation assay to rapidly identify and quantify the C. auris burden in easily obtained patient samples and produce a quantitative snapshot of the AMR landscape in individual samples,” said Wyss Founding Core Faculty member James Collins, PhD. “This has not been possible using previous diagnostic methods and is a technological feat that, in addition to CRISPR engineering, required us to deeply integrate the SHERLOCK technology with the Walt group’s cutting-edge single molecule detection technology and a tailored machine learning approach.”
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