Journal
MOLECULAR ECOLOGY RESOURCES
Volume -, Issue -, Pages -Publisher
WILEY
DOI: 10.1111/1755-0998.13902
Keywords
field-applicable; isothermal amplification; onsite diagnostic tool; recombinase polymerase amplification; wildlife disease
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This study presents the development of a CRISPR/Cas-based biosensor for detecting the fungal agent Pd, which causes white-nose syndrome in bats. The biosensor combines RPA and CRISPR/Cas12a nuclease cleavage to detect Pd DNA from bat samples. It has the potential to aid in the rapid identification of Pd-positive sites in the field.
The accessibility to CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein) genetic tools has given rise to applications beyond site-directed genome editing for the detection of DNA and RNA. These tools include precise diagnostic detection of human disease pathogens, such as SARS-CoV-2 and Zika virus. Despite the technology being rapid and cost-effective, the use of CRISPR/Cas tools in the surveillance of the causative agents of wildlife diseases has not been prominent. This study presents the development of a minimally invasive, field-applicable and user-friendly CRISPR/Cas-based biosensor for the detection of Pseudogymnoascus destructans (Pd), the causative fungal agent of white-nose syndrome (WNS), an infectious disease that has killed more than five million bats in North America since its discovery in 2006. The biosensor assay combines a recombinase polymerase amplification (RPA) step followed by CRISPR/Cas12a nuclease cleavage to detect Pd DNA from bat dermal swab and guano samples. The biosensor had similar detection results when compared to quantitative PCR in distinguishing Pd-positive versus negative field samples. Although bat dermal swabs could be analysed with the biosensor without nucleic acid extraction, DNA extraction was needed when screening guano samples to overcome inhibitors. This assay can be applied to help with more rapid delineation of Pd-positive sites in the field to inform management decisions. With further optimization, this technology has broad translation potential to wildlife disease-associated pathogen detection and monitoring applications.
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