Digital CRISPR-based diagnostics for quantification of Candida auris and resistance mutations

Area of Science:

• Molecular Biology
• Diagnostic Technology
• Mycology

Background:

• Candida auris is a growing global health threat requiring rapid diagnostics.
• Accurate antifungal susceptibility testing is crucial for effective treatment of C. auris infections.

Purpose of the Study:

• To develop and validate a novel diagnostic platform, digital SHERLOCK (dSHERLOCK), for rapid identification and antifungal susceptibility testing of Candida auris.
• To assess the sensitivity, specificity, and speed of dSHERLOCK for C. auris detection and resistance mutation identification.

Main Methods:

• Utilized CRISPR/Cas nucleic acid detection combined with single-template quantification and real-time kinetics monitoring.
• Developed assays to detect key mutations associated with azole and echinocandin resistance in C. auris.
• Employed machine learning and kinetic monitoring for accurate quantification of mixed mutant and wild-type alleles.

Main Results:

• dSHERLOCK demonstrated high sensitivity across major C. auris clades (1-4) and specificity against other fungi.
• Rapid detection of C. auris in 20 minutes from swab samples, with quantification achieved in 40 minutes.
• Accurate identification of mixed fungal populations with varying antifungal susceptibility using kinetic analysis of FKS1 SNP alleles.

Conclusions:

• The dSHERLOCK platform provides a rapid, sensitive, and specific method for C. auris identification and antifungal resistance testing.
• The technology's reliance on common lab materials facilitates widespread global deployment in healthcare settings.
• This advancement has the potential to significantly improve patient management and treatment outcomes for C. auris infections.